Author name cluster

Ioannis M. Rekleitis

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

56 papers

2 author rows

ICRA Conference 2025 Conference Paper

ODYSSEE: Oyster Detection Yielded by Sensor Systems on Edge Electronics

Xiaomin Lin 0002
Vivek Mange
Arjun Suresh
Bernhard Neuberger
Aadi Palnitkar
Brendan Campbell
Alan Williams
Kleio Baxevani

Oysters are a vital keystone species in coastal ecosystems, providing significant economic, environmental, and cultural benefits. As the importance of oysters grows, so does the relevance of autonomous systems for their detection and monitoring. However, current monitoring strategies often rely on destructive methods. While manual identification of oysters from video footage is non-destructive, it is time-consuming, requires expert input, and is further complicated by the challenges of the underwater environment. To address these challenges, we propose a novel pipeline using stable diffusion to augment a collected real dataset with photorealistic synthetic data. This method enhances the dataset used to train a YOLOv10-based vision model. The model is then deployed and tested on an edge platform; Aqua2, an Autonomous Underwater Vehicle (AUV), achieving a state-of-the-art 0. 657 mAP@50 for oyster detection.

ICRA Conference 2024 Conference Paper

CaveSeg: Deep Semantic Segmentation and Scene Parsing for Autonomous Underwater Cave Exploration

Adnan Abdullah
Titon Barua
Reagan Tibbetts
Zijie Chen
Md Jahidul Islam
Ioannis M. Rekleitis

In this paper, we present CaveSeg - the first visual learning pipeline for semantic segmentation and scene parsing for AUV navigation inside underwater caves. We address the problem of scarce annotated training data by preparing a comprehensive dataset for semantic segmentation of underwater cave scenes. It contains pixel annotations for important navigation markers (e. g. caveline, arrows), obstacles (e. g. ground plain and overhead layers), scuba divers, and open areas for servoing. Through comprehensive benchmark analyses on cave systems in USA, Mexico, and Spain locations, we demonstrate that robust deep visual models can be developed based on CaveSeg for fast semantic scene parsing of underwater cave environments. In particular, we formulate a novel transformer-based model that is computationally light and offers near real-time execution in addition to achieving state-of-the-art performance. Finally, we explore the design choices and implications of semantic segmentation for visual servoing by AUVs inside underwater caves. The proposed model and benchmark dataset open up promising opportunities for future research in autonomous underwater cave exploration and mapping.

ICRA Conference 2024 Conference Paper

Enhancing Visual Inertial SLAM with Magnetic Measurements

Bharat Joshi
Ioannis M. Rekleitis

This paper presents an extension to visual inertial odometry (VIO) by introducing tightly-coupled fusion of magnetometer measurements. A sliding window of keyframes is optimized by minimizing re-projection errors, relative inertial errors, and relative magnetometer orientation errors. The results of IMU orientation propagation are used to efficiently transform magnetometer measurements between frames producing relative orientation constraints between consecutive frames. The soft and hard iron effects are calibrated using an ellipsoid fitting algorithm. The introduction of magnetometer data results in significant reductions in the orientation error and also in recovery of the true yaw orientation with respect to the magnetic north. The proposed framework operates in all environments with slow-varying magnetic fields, mainly outdoors and underwater. We have focused our work on the underwater domain, especially in underwater caves, as the narrow passage and turbulent flow make it difficult to perform loop closures and reset the localization drift. The underwater caves present challenges to VIO due to the absence of ambient light and the confined nature of the environment, while also being a crucial source of fresh water and providing valuable historical records. Experimental results from underwater caves demonstrate the improvements in accuracy and robustness introduced by the proposed VIO extension.

ICRA Conference 2023 Conference Paper

3-D Reconstruction Using Monocular Camera and Lights: Multi-View Photometric Stereo for Non-Stationary Robots

Monika Roznere
Philippos Mordohai
Ioannis M. Rekleitis
Alberto Quattrini Li

This paper proposes a novel underwater Multi-View Photometric Stereo (MVPS) framework for reconstructing scenes in 3-D with a non-stationary low-cost robot equipped with a monocular camera and fixed lights. The underwater realm is the primary focus of study here, due to the challenges in utilizing underwater camera imagery and lack of low-cost reliable localization systems. Previous underwater PS approaches provided accurate scene reconstruction results, but assumed that the robot was stationary at the bottom. This assumption is limiting, as many artifacts, reefs, and man-made structures are large and meters above the bottom. Our proposed MVPS framework relaxes the stationarity assumption by utilizing a monocular SLAM system to estimate small robot motions and extract an initial sparse feature map. To compensate for the scale inconsistency in monocular SLAM output, our MVPS optimization scheme collectively estimates a high-quality, dense 3-D reconstruction and corrects the camera pose estimates. We also present an attenuation and camera-light extrinsic parameter calibration method for non-stationary robots. Finally, validation experiments with a BlueROV2 demonstrated the low-cost capability of producing high-quality scene reconstructions. Overall, this work is the foundation of an active perception pipeline for robots (i. e. , underwater, ground, and aerial) to explore and map complex structures in high accuracy and resolution with an inexpensive sensor-light configuration.

ICRA Conference 2023 Conference Paper

Real-Time Dense 3D Mapping of Underwater Environments

Weihan Wang
Bharat Joshi
Nathaniel Burgdorfer
Konstantinos Batsos
Alberto Quattrini Li
Philippos Mordohai
Ioannis M. Rekleitis

This paper addresses real-time dense 3D reconstruction for a resource-constrained Autonomous Underwater Vehicle (AUV). Underwater vision-guided operations are among the most challenging as they combine 3D motion in the presence of external forces, limited visibility, and absence of global positioning. Obstacle avoidance and effective path planning require online dense reconstructions of the environment. Autonomous operation is central to environmental monitoring, marine archaeology, resource utilization, and underwater cave exploration. To address this problem, we propose to use SVIn2, a robust VIO method, together with a real-time 3D reconstruction pipeline. We provide extensive evaluation on four challenging underwater datasets. Our pipeline produces comparable reconstruction with that of COLMAP, the state-of-the-art offline 3D reconstruction method, at high frame rates on a single CPU.

ICRA Conference 2023 Conference Paper

SM/VIO: Robust Underwater State Estimation Switching Between Model-based and Visual Inertial Odometry

Bharat Joshi
Hunter Damron
Sharmin Rahman
Ioannis M. Rekleitis

This paper addresses the robustness problem of visual-inertial state estimation for underwater operations. Underwater robots operating in a challenging environment are required to know their pose at all times. All vision-based localization schemes are prone to failure due to poor visibility conditions, color loss, and lack of features. The proposed approach utilizes a model of the robot's kinematics together with proprioceptive sensors to maintain the pose estimate during visual-inertial odometry (VIO) failures. Furthermore, the trajectories from successful VIO and the ones from the model-driven odometry are integrated in a coherent set that maintains a consistent pose at all times. Health-monitoring tracks the VIO process ensuring timely switches between the two estimators. Finally, loop closure is implemented on the overall trajectory. The resulting framework is a robust estimator switching between model-based and visual-inertial odometry (SM/VIO). Experimental results from numerous deployments of the Aqua2 vehicle demonstrate the robustness of our approach over coral reefs and a shipwreck.

IROS Conference 2023 Conference Paper

Weakly Supervised Caveline Detection for AUV Navigation Inside Underwater Caves

Boxiao Yu
Reagan Tibbetts
Titon Barua
Ailani Morales
Ioannis M. Rekleitis
Md Jahidul Islam

Underwater caves are challenging environments that are crucial for water resource management, and for our understanding of hydro-geology and history. Mapping underwater caves is a time-consuming, labor-intensive, and hazardous operation. For autonomous cave mapping by underwater robots, the major challenge lies in vision-based estimation in the complete absence of ambient light, which results in constantly moving shadows due to the motion of the camera-light setup. Thus, detecting and following the caveline as navigation guidance is paramount for robots in autonomous cave mapping missions. In this paper, we present a computationally light caveline detection model based on a novel Vision Transformer (ViT)-based learning pipeline. We address the problem of scarce annotated training data by a weakly supervised formulation where the learning is reinforced through a series of noisy predictions from intermediate sub-optimal models. We validate the utility and effectiveness of such weak supervision for caveline detection and tracking in three different cave locations: USA, Mexico, and Spain. Experimental results demonstrate that our proposed model, CL-ViT, balances the robustness-efficiency trade-off, ensuring good generalization performance while offering 10+ FPS on single-board (Jetson TX2) devices.

IROS Conference 2022 Conference Paper

Confined Water Body Coverage under Resource Constraints

Ibrahim Salman
Jason Raiti
Nare Karapetyan
Archana Venkatachari
Annie Bourbonnais
Jason M. O'Kane
Ioannis M. Rekleitis

This paper presents a novel algorithm for monitoring marine environments utilizing a resource-constrained robot. Collecting water quality data from large bodies of water is paramount for monitoring the ecosystem's health, particularly for predicting harmful cyanobacteria blooms. The large spatial dimensions of such bodies of water and the slow varying of water quality parameters make exhaustive, complete coverage impractical and unnecessary. This work explores a new strategy for efficiently measuring water quality quantities with an autonomous surface vehicle (ASV). The method utilizes the medial axis of the water body producing a guideline for the ASV trajectory that visits representative areas of the environment. The proposed method ensures data collection in the narrower parts of the lake, where researchers have historically observed harmful blooms while also visiting open water areas. It also presents an analysis of the Spatio-temporal sensitivity of the target sensor. A comparison with the traditional lawnmower algorithm demonstrates that the conventional BCD-based complete coverage method cannot sample the small coves of a lake. As such, we show that the proposed method captures more diverse regions of the area with a partial coverage technique. Offline analysis of several lakes and reservoirs and results from field deployments at Lake Murray, SC, USA, demonstrate the proposed method's effectiveness.

ICRA Conference 2022 Conference Paper

High Definition, Inexpensive, Underwater Mapping

Bharat Joshi
Marios Xanthidis
Sharmin Rahman
Ioannis M. Rekleitis

In this paper we present a complete framework for Underwater SLAM utilizing a single inexpensive sensor. Over the recent years, imaging technology of action cameras is producing stunning results even under the challenging conditions of the underwater domain. The GoPro 9 camera provides high definition video in synchronization with an Inertial Measurement Unit (IMU) data stream encoded in a single mp4 file. The visual inertial SLAM framework is augmented to adjust the map after each loop closure. Data collected at an artificial wreck of the coast of South Carolina and in caverns and caves in Florida demonstrate the robustness of the proposed approach in a variety of conditions.

IROS Conference 2021 Conference Paper

AquaVis: A Perception-Aware Autonomous Navigation Framework for Underwater Vehicles

Marios Xanthidis
Michail Kalaitzakis
Nare Karapetyan
James Johnson
Nikolaos I. Vitzilaios
Jason M. O'Kane
Ioannis M. Rekleitis

Visual monitoring operations underwater require both observing the objects of interest in close-proximity, and tracking the few feature-rich areas necessary for state estimation. This paper introduces the first navigation framework, called AquaVis, that produces on-line visibility-aware motion plans that enable Autonomous Underwater Vehicles (AUVs) to track multiple visual objectives with an arbitrary camera configuration in real-time. Using the proposed pipeline, AUVs can efficiently move in 3D, reach their goals while avoiding obstacles safely, and maximizing the visibility of multiple objectives along the path within a specified proximity. The method is sufficiently fast to be executed in real-time and is suitable for single or multiple camera configurations. Experimental results show the significant improvement on tracking multiple automatically-extracted points of interest, with low computational overhead and fast re-planning times. Accompanying short video: https://youtu.be/JKObbrIZyU

IROS Conference 2020 Conference Paper

DeepURL: Deep Pose Estimation Framework for Underwater Relative Localization

Bharat Joshi
Md. Modasshir
Travis Manderson
Hunter Damron
Marios Xanthidis
Alberto Quattrini Li
Ioannis M. Rekleitis
Gregory Dudek

In this paper, we propose a real-time deep learning approach for determining the 6D relative pose of Autonomous Underwater Vehicles (AUV) from a single image. A team of autonomous robots localizing themselves in a communication-constrained underwater environment is essential for many applications such as underwater exploration, mapping, multi-robot convoying, and other multi-robot tasks. Due to the profound difficulty of collecting ground truth images with accurate 6D poses underwater, this work utilizes rendered images from the Unreal Game Engine simulation for training. An image-to-image translation network is employed to bridge the gap between the rendered and the real images producing synthetic images for training. The proposed method predicts the 6D pose of an AUV from a single image as 2D image keypoints representing 8 corners of the 3D model of the AUV, and then the 6D pose in the camera coordinates is determined using RANSAC-based PnP. Experimental results in real-world underwater environments (swimming pool and ocean) with different cameras demonstrate the robustness and accuracy of the proposed technique in terms of translation error and orientation error over the state-of-the-art methods. The code is publicly available.

ICRA Conference 2020 Conference Paper

Enhancing Coral Reef Monitoring Utilizing a Deep Semi-Supervised Learning Approach

Md. Modasshir
Ioannis M. Rekleitis

Coral species detection underwater is a challenging problem. There are many cases when even the experts (marine biologists) fail to recognize corals, hence limiting ground truth annotation for training a robust detection system. Identifying coral species is fundamental for enabling the monitoring of coral reefs, a task currently performed by humans, which can be automated with the use of underwater robots. By employing temporal cues using a tracker on a high confidence prediction by a convolutional neural network-based object detector, we augment the collected dataset for the retraining of the object detector. However, using trackers to extract examples also introduces hard or mislabelled samples, which is counterproductive and will deteriorate the performance of the detector. In this work, we show that employing a simple deep neural network to filter out hard or mislabelled samples can help regulate sample extraction. We empirically evaluate our approach in a coral object dataset, collected via an Autonomous Underwater Vehicle (AUV) and human divers, that shows the benefit of incorporating extracted examples obtained from tracking. This work also demonstrates how controlling sample generation by tracking using a simple deep neural network can further improve an object detector.

ICRA Conference 2020 Conference Paper

Navigation in the Presence of Obstacles for an Agile Autonomous Underwater Vehicle

Marios Xanthidis
Nare Karapetyan
Hunter Damron
Sharmin Rahman
James Johnson
Allison O'Connell
Jason M. O'Kane
Ioannis M. Rekleitis

Navigation underwater traditionally is done by keeping a safe distance from obstacles, resulting in "fly-overs" of the area of interest. Movement of an autonomous underwater vehicle (AUV) through a cluttered space, such as a shipwreck or a decorated cave, is an extremely challenging problem that has not been addressed in the past. This paper proposes a novel navigation framework utilizing an enhanced version of Trajopt for fast 3D path-optimization planning for AUVs. A sampling-based correction procedure ensures that the planning is not constrained by local minima, enabling navigation through narrow spaces. Two different modalities are proposed: planning with a known map results in efficient trajectories through cluttered spaces; operating in an unknown environment utilizes the point cloud from the visual features detected to navigate efficiently while avoiding the detected obstacles. The proposed approach is rigorously tested, both on simulation and in-pool experiments, proven to be fast enough to enable safe real-time 3D autonomous navigation for an AUV.

IROS Conference 2019 Conference Paper

Contour based Reconstruction of Underwater Structures Using Sonar, Visual, Inertial, and Depth Sensor

Sharmin Rahman
Alberto Quattrini Li
Ioannis M. Rekleitis

This paper presents a systematic approach on realtime reconstruction of an underwater environment using Sonar, Visual, Inertial, and Depth data. In particular, low lighting conditions, or even complete absence of natural light inside caves, results in strong lighting variations, e. g. , the cone of the artificial video light intersecting underwater structures, and the shadow contours. The proposed method utilizes the well defined edges between well lit areas and darkness to provide additional features, resulting into a denser 3D point cloud than the usual point clouds from a visual odometry system. Experimental results in an underwater cave at Ginnie Springs, FL, with a custom-made underwater sensor suite demonstrate the performance of our system. This will enable more robust navigation of autonomous underwater vehicles using the denser 3D point cloud to detect obstacles and achieve higher resolution reconstructions.

IROS Conference 2019 Conference Paper

Experimental Comparison of Open Source Visual-Inertial-Based State Estimation Algorithms in the Underwater Domain

Bharat Joshi
Nikolaos I. Vitzilaios
Ioannis M. Rekleitis
Sharmin Rahman
Michail Kalaitzakis
Brennan Cain
James Johnson
Marios Xanthidis

A plethora of state estimation techniques have appeared in the last decade using visual data, and more recently with added inertial data. Datasets typically used for evaluation include indoor and urban environments, where supporting videos have shown impressive performance. However, such techniques have not been fully evaluated in challenging conditions, such as the marine domain. In this paper, we compare ten recent open-source packages to provide insights on their performance and guidelines on addressing current challenges. Specifically, we selected direct and indirect methods that fuse camera and Inertial Measurement Unit (IMU) data together. Experiments are conducted by testing all packages on datasets collected over the years with underwater robots in our laboratory. All the datasets are made available online.

IROS Conference 2019 Conference Paper

Riverine Coverage with an Autonomous Surface Vehicle over Known Environments

Nare Karapetyan
Adam Braude
Jason Moulton
Joshua A. Burstein
Scott White
Jason M. O'Kane
Ioannis M. Rekleitis

Environmental monitoring and surveying operations on rivers currently are performed primarily with manually-operated boats. In this domain, autonomous coverage of areas is of vital importance, for improving both the quality and the efficiency of coverage. This paper leverages human expertise in river exploration and data collection strategies to automate and optimize these processes using autonomous surface vehicles (ASVs). In particular, three deterministic algorithms for both partial and complete coverage of a river segment are proposed, providing varying path length, coverage density, and turning patterns. These strategies resulted in increases in accuracy and efficiency compared to human performance. The proposed methods were extensively tested in simulation using maps of real rivers of different shapes and sizes. In addition, to verify their performance in real world operations, the algorithms were deployed successfully on several parts of the Congaree River in South Carolina, USA, resulting in total of more than 35km of coverage trajectories in the field.

IROS Conference 2019 Conference Paper

SVIn2: An Underwater SLAM System using Sonar, Visual, Inertial, and Depth Sensor

Sharmin Rahman
Alberto Quattrini Li
Ioannis M. Rekleitis

This paper presents a novel tightly-coupled keyframe-based Simultaneous Localization and Mapping (SLAM) system with loop-closing and relocalization capabilities targeted for the underwater domain. Our previous work, SVIn, augmented the state-of-the-art visual-inertial state estimation package OKVIS to accommodate acoustic data from sonar in a non-linear optimization-based framework. This paper addresses drift and loss of localization – one of the main problems affecting other packages in underwater domain – by providing the following main contributions: a robust initialization method to refine scale using depth measurements, a fast preprocessing step to enhance the image quality, and a real-time loop-closing and relocalization method using bag of words (BoW). An additional contribution is the addition of depth measurements from a pressure sensor to the tightly-coupled optimization formulation. Experimental results on datasets collected with a custom-made underwater sensor suite and an autonomous underwater vehicle from challenging underwater environments with poor visibility demonstrate performance never achieved before in terms of accuracy and robustness.

ICRA Conference 2018 Conference Paper

Heterogeneous Multi-Robot System for Exploration and Strategic Water Sampling

Sandeep Manjanna
Alberto Quattrini Li
Ryan N. Smith
Ioannis M. Rekleitis
Gregory Dudek

Physical sampling of water for off-site analysis is necessary for many applications like monitoring the quality of drinking water in reservoirs, understanding marine ecosystems, and measuring contamination levels in fresh-water systems. In this paper, the focus is on algorithms for efficient measurement and sampling using a multi-robot, data-driven, water-sampling behavior, where autonomous surface vehicles plan and execute water sampling using the chlorophyll density as a cue for plankton-rich water samples. We use two Autonomous Surface Vehicles (ASVs), one equipped with a water quality sensor and the other equipped with a water-sampling apparatus. The ASV with the sensor acts as an explorer, measuring and building a spatial map of chlorophyll density in the given region of interest. The ASV equipped with the water sampling apparatus makes decisions in real time on where to sample the water based on the suggestions made by the explorer robot. We evaluate the system in the context of measuring chlorophyll distributions. We do this both in simulation based on real geophysical data from MODIS measurements, and on real robots in a water reservoir. We demonstrate the effectiveness of the proposed approach in several ways including in terms of mean error in the interpolated data as a function of distance traveled.

ICRA Conference 2018 Conference Paper

Multi-robot Dubins Coverage with Autonomous Surface Vehicles

Nare Karapetyan
Jason Moulton
Jeremy S. Lewis
Alberto Quattrini Li
Jason M. O'Kane
Ioannis M. Rekleitis

In large scale coverage operations, such as marine exploration or aerial monitoring, single robot approaches are not ideal, as they may take too long to cover a large area. In such scenarios, multi-robot approaches are preferable. Furthermore, several real world vehicles are non-holonomic, but can be modeled using Dubins vehicle kinematics. This paper focuses on environmental monitoring of aquatic environments using Autonomous Surface Vehicles (ASVs). In particular, we propose a novel approach for solving the problem of complete coverage of a known environment by a multi-robot team consisting of Dubins vehicles. It is worth noting that both multi-robot coverage and Dubins vehicle coverage are NP-complete problems. As such, we present two heuristics methods based on a variant of the traveling salesman problem-k-TSP-formulation and clustering algorithms that efficiently solve the problem. The proposed methods are tested both in simulations to assess their scalability and with a team of ASVs operating on a 200 km 2 lake to ensure their applicability in real world.

ICRA Conference 2018 Conference Paper

Sonar Visual Inertial SLAM of Underwater Structures

Sharmin Rahman
Alberto Quattrini Li
Ioannis M. Rekleitis

This paper presents an extension to a state of the art Visual-Inertial state estimation package (OKVIS) in order to accommodate data from an underwater acoustic sensor. Mapping underwater structures is important in several fields, such as marine archaeology, search and rescue, resource management, hydrogeology, and speleology. Collecting the data, however, is a challenging, dangerous, and exhausting task. The underwater domain presents unique challenges in the quality of the visual data available; as such, augmenting the exteroceptive sensing with acoustic range data results in improved reconstructions of the underwater structures. Experimental results from underwater wrecks, an underwater cave, and a submerged bus demonstrate the performance of our approach.

IROS Conference 2018 Conference Paper

Underwater Surveying via Bearing Only Cooperative Localization

Hunter Damron
Alberto Quattrini Li
Ioannis M. Rekleitis

Bearing only cooperative localization has been used successfully on aerial and ground vehicles. In this paper we present an extension of the approach to the underwater domain. The focus is on adapting the technique to handle the challenging visibility conditions underwater. Furthermore, data from inertial, magnetic, and depth sensors are utilized to improve the robustness of the estimation. In addition to robotic applications, the presented technique can be used for cave mapping and for marine archeology surveying, both by human divers. Experimental results from different environments, including a fresh water, low visibility, lake in South Carolina; a cavern in Florida; and coral reefs in Barbados during the day and during the night, validate the robustness and the accuracy of the proposed approach.

IROS Conference 2017 Conference Paper

Efficient multi-robot coverage of a known environment

Nare Karapetyan
Kelly Benson
Chris McKinney
Perouz Taslakian
Ioannis M. Rekleitis

This paper addresses the complete area coverage problem of a known environment by multiple-robots. Complete area coverage is the problem of moving an end-effector over all available space while avoiding existing obstacles. In such tasks, using multiple robots can increase the efficiency of the area coverage in terms of minimizing the operational time and increase the robustness in the face of robot attrition. Unfortunately, the problem of finding an optimal solution for such an area coverage problem with multiple robots is known to be NP-complete. In this paper we present two approximation heuristics for solving the multi-robot coverage problem. The first solution presented is a direct extension of an efficient single robot area coverage algorithm, based on an exact cellular decomposition. The second algorithm is a greedy approach that divides the area into equal regions and applies an efficient single-robot coverage algorithm to each region. We present experimental results for two algorithms. Results indicate that our approaches provide good coverage distribution between robots and minimize the workload per robot, meanwhile ensuring complete coverage of the area.

ICRA Conference 2017 Conference Paper

Multirobot online construction of communication maps

Jacopo Banfi
Alberto Quattrini Li
Nicola Basilico
Ioannis M. Rekleitis
Francesco Amigoni

The importance of communication in many multirobot information-gathering tasks requires the availability of reliable communication maps. These provide estimates of the radio signal strength and can be used to predict the presence of communication links between different locations of the environment. In the problem we consider, a team of mobile robots has to build such maps autonomously in a robot-to-robot communication setting. The solution we propose models the signal's distribution with a Gaussian Process and exploits different online sensing strategies to coordinate and guide the robots during their data acquisition. Our methods show interesting operative insights both in simulations and on real TurtleBot 2 platforms.

IROS Conference 2017 Conference Paper

Semi-boustrophedon coverage with a dubins vehicle

Jeremy S. Lewis
William Edwards
Kelly Benson
Ioannis M. Rekleitis
Jason M. O'Kane

This paper addresses the problem of generating coverage paths-that is, paths that pass within some sensor footprint of every point in an environment-for vehicles with Dubins motion constraints. We extend previous work that solves this coverage problem as a traveling salesman problem (TSP) by introducing a practical heuristic algorithm to reduce runtime while maintaining near-optimal path length. Furthermore, we show that generating an optimal coverage path is NP-hard by reducing from the Exact Cover problem, which provides justification for our algorithm's conversion of Dubins coverage instances to TSP instances. Extensive experiments demonstrate that the algorithm does indeed produce length paths comparable to optimal in significantly less time.

ICRA Conference 2017 Conference Paper

Underwater cave mapping using stereo vision

Nick Weidner
Sharmin Rahman
Alberto Quattrini Li
Ioannis M. Rekleitis

This paper presents a systematic approach for the 3-D mapping of underwater caves. Exploration of underwater caves is very important for furthering our understanding of hydrogeology, managing efficiently water resources, and advancing our knowledge in marine archaeology. Underwater cave exploration by human divers however, is a tedious, labor intensive, extremely dangerous operation, and requires highly skilled people. As such, it is an excellent fit for robotic technology, which has never before been addressed. In addition to the underwater vision constraints, cave mapping presents extra challenges in the form of lack of natural illumination and harsh contrasts, resulting in failure for most of the state-of-the-art visual based state estimation packages. A new approach employing a stereo camera and a video-light is presented. Our approach utilizes the intersection of the cone of the video-light with the cave boundaries: walls, floor, and ceiling, resulting in the construction of a wire frame outline of the cave. Successive frames are combined using a state of the art visual odometry algorithm while simultaneously inferring scale through the stereo reconstruction. Results from experiments at a cave, part of the Sistema Camilo, Quintana Roo, Mexico, validate our approach. The cave wall reconstruction presented provides an immersive experience in 3-D.

IROS Conference 2016 Conference Paper

Active localization with dynamic obstacles

Alberto Quattrini Li
Marios Xanthidis
Jason M. O'Kane
Ioannis M. Rekleitis

This paper addresses the problem of robot global localization in a known environment, in the presence of many dynamic obstacles. Deploying a robot in crowded spaces such as museums, shopping malls, department stores, or university campuses is especially challenging because the moving people occlude the static parts of the environment, such as walls and doorways, making the robot essentially blind. A new weighting function is proposed for a particle filter state estimation algorithm that accounts for the presence of dynamic obstacles and avoids population depletion. An active localization strategy is employed which guides the robot to locations that resolve ambiguities and eliminate hypotheses in a systematic manner. Experimental results from multiple simulations and from real robot deployments validate the localization improvements achieved by the proposed method.

ICRA Conference 2016 Conference Paper

Asynchronous multirobot exploration under recurrent connectivity constraints

Jacopo Banfi
Alberto Quattrini Li
Nicola Basilico
Ioannis M. Rekleitis
Francesco Amigoni

In multirobot exploration under centralized control, communication plays an important role in constraining the team exploration strategy. Recurrent connectivity is a way to define communication constraints for which robots must connect to a base station only when making new observations. This paper studies effective multirobot exploration strategies under recurrent connectivity by considering a centralized and asynchronous planning framework. We formalize the problem of selecting the optimal set of locations robots should reach, provide an exact formulation to solve it, and devise an approximation algorithm to obtain efficient solutions with a bounded loss of optimality. Experiments in simulation and on real robots evaluate our approach in a number of settings.

IROS Conference 2015 Conference Paper

Robust environment mapping using flux skeletons

Morteza Rezanejad
Babak Samari
Ioannis M. Rekleitis
Kaleem Siddiqi
Gregory Dudek

We consider how to directly extract a road map (also known as a topological representation) of an initially-unknown 2-dimensional environment via an on-line procedure which robustly computes a retraction of its boundaries. While such approaches are well known for their theoretical elegance, computing such representations in practice is complicated when the data is sparse and noisy. In this paper we present the online construction of a topological map and the implementation of a control law for guiding the robot to the nearest unexplored area. The proposed method operates by allowing the robot to localize itself on a partially constructed map, calculate a path to unexplored parts of the environment (frontiers), compute a robust terminating condition when the robot has fully explored the environment, and achieve loop closure detection. The proposed algorithm results in smooth safe paths for the robot's navigation needs. The presented approach is an any-time-algorithm which allows for the active creation of topological maps from laser-scan data, as it is being acquired. The resulting map is stable under variations to noise and the initial conditions. The key idea is the use of a flux-based skeletonization algorithm on the latest occupancy grid map. We also propose a navigation strategy based on a heuristic where the robot is directed towards nodes in the topological map that open to empty space. The method is evaluated on both synthetic data and in the context of active exploration using a Turtlebot 2. Our results demonstrate complete mapping of different environments with smooth topological abstraction without spurious edges.

IROS Conference 2014 Conference Paper

Ear-based exploration on hybrid metric/topological maps

Qiwen Zhang
David Whitney
Florian Shkurti
Ioannis M. Rekleitis

In this paper we propose a hierarchy of techniques for performing loop closure in indoor environments together with an exploration strategy designed to reduce uncertainty in the resulting map. We use the generalized Voronoi graph to represent the indoor environment and an extended Kalman filter to track the pose of the robot and the position of the junctions (vertices) of the topological graph. Every time a vertex is revisited, the robot re-localizes and updates the uncertainty estimate accordingly. Finally, since the reduction of the map uncertainty remains one of the main concerns, the robot will optimize its schedule of revisiting junctions in the environment in order to reduce the accumulated uncertainty. Experimental results from a mobile robot equipped with a laser range-finder and results from realistic simulations that validate our approach are presented.

IROS Conference 2012 Conference Paper

I see you, you see me: Cooperative localization through bearing-only mutually observing robots

Philippe Giguère
Ioannis M. Rekleitis
Maxime Latulippe

Cooperative localization is one of the fundamental techniques in GPS-denied environments, such as underwater, indoor, or on other planets, where teams of robots use each other to improve their pose estimation. In this paper, we present a novel schema for performing cooperative localization using bearing only measurements. These measurements correspond to the angles of pairs of landmarks located on each robot, extracted from camera images. Thus, the only exteroceptive measurements used are the camera images taken by each robot, under the condition that both cameras are mutually visible. An analytical solution is derived, together with an analysis of uncertainty as a function to the relative pose of the robots. A theoretical comparison with a standard stereo camera pose reconstruction is also provided. Finally, the feasibility and performance of the proposed method were validated, through simulations and experiments with a mobile robot setup.

IROS Conference 2012 Conference Paper

Multi-domain monitoring of marine environments using a heterogeneous robot team

Florian Shkurti
Anqi Xu 0003
Malika Meghjani
Juan Camilo Gamboa Higuera
Yogesh A. Girdhar
Philippe Giguère
Bir Bikram Dey
Jimmy Li 0001

In this paper we describe a heterogeneous multi-robot system for assisting scientists in environmental monitoring tasks, such as the inspection of marine ecosystems. This team of robots is comprised of a fixed-wing aerial vehicle, an autonomous airboat, and an agile legged underwater robot. These robots interact with off-site scientists and operate in a hierarchical structure to autonomously collect visual footage of interesting underwater regions, from multiple scales and mediums. We discuss organizational and scheduling complexities associated with multi-robot experiments in a field robotics setting. We also present results from our field trials, where we demonstrated the use of this heterogeneous robot team to achieve multi-domain monitoring of coral reefs, based on real-time interaction with a remotely-located marine biologist.

IROS Conference 2011 Conference Paper

MARE: Marine Autonomous Robotic Explorer

Yogesh A. Girdhar
Anqi Xu 0003
Bir Bikram Dey
Malika Meghjani
Florian Shkurti
Ioannis M. Rekleitis
Gregory Dudek

We present MARE, an autonomous airboat robot that is suitable for exploration-oriented tasks, such as inspection of coral reefs and shallow seabeds. The combination of this platform's particular mechanical properties and its powerful software framework enables it to function in a multitude of potential capacities, including autonomous surveillance, mapping, and search operations. In this paper we describe two different exploration strategies and their implementation using the MARE platform. First, we discuss the application of an efficient coverage algorithm, for the purpose of achieving systematic exploration of a known and bounded environment. Second, we present an exploration strategy driven by surprise, which steers the robot on a path that might lead to potentially surprising observations.

ICRA Conference 2011 Conference Paper

Optimal complete terrain coverage using an Unmanned Aerial Vehicle

Anqi Xu 0003
Chatavut Viriyasuthee
Ioannis M. Rekleitis

We present the adaptation of an optimal terrain coverage algorithm for the aerial robotics domain. The general strategy involves computing a trajectory through a known environment with obstacles that ensures complete coverage of the terrain while minimizing path repetition. We introduce a system that applies and extends this generic algorithm to achieve automated terrain coverage using an aerial vehicle. Extensive experimental results in simulation validate the presented system, along with data from over 100 kilometers of successful coverage flights using a fixed-wing aircraft.

IROS Conference 2011 Conference Paper

State estimation of an underwater robot using visual and inertial information

Florian Shkurti
Ioannis M. Rekleitis
Milena Scaccia
Gregory Dudek

This paper presents an adaptation of a vision and inertial-based state estimation algorithm for use in an underwater robot. The proposed approach combines information from an Inertial Measurement Unit (IMU) in the form of linear accelerations and angular velocities, depth data from a pressure sensor, and feature tracking from a monocular downward facing camera to estimate the 6DOF pose of the vehicle. To validate the approach, we present extensive experimental results from field trials conducted in underwater environments with varying lighting and visibility conditions, and we demonstrate successful application of the technique underwater.

ICRA Conference 2010 Conference Paper

Optimal coverage of a known arbitrary environment

Raphael Mannadiar
Ioannis M. Rekleitis

The problem of coverage of known space by a mobile robot has many applications. Of particular interest is providing a solution that guarantees the complete coverage of the free space by traversing an optimal path, in terms of the distance travelled. In this paper we introduce a new algorithm based on the Boustrophedon cellular decomposition. The presented algorithm encodes the areas (cells) to be covered as edges of the Reeb graph. The optimal solution to the Chinese Postman Problem (CPP) is used to calculate an Euler tour, which guarantees complete coverage of the available free space while minimizing the path of the robot. In addition, we extend the classical solution of the CPP to account for the entry point of the robot for cell coverage by changing the weights of the Reeb graph edges. Proof of correctness is provided together with experimental results in different environments.

ICRA Conference 2009 Conference Paper

Autonomous planetary exploration using LIDAR data

Ioannis M. Rekleitis
Jean-Luc Bedwani
Erick Dupuis

In this paper we present the approach for autonomous planetary exploration developed at the Canadian Space Agency. The goal of this work is to autonomously navigate to remote locations, well beyond the sensing horizon of the rover, with minimal interaction with a human operator. We employ LIDAR range sensors due to their accuracy, long range and robustness in the harsh lighting conditions of space. Irregular triangular meshes (ITMs) are used for representing the environment providing an accurate yet compact spatial representation. In this paper a novel path-planning technique through the ITM is introduced, which guides the rover through flatter terrain and safely away from obstacles. Experiments performed in CSA's Mars emulation terrain that validate our approach are also presented.

ICRA Conference 2009 Conference Paper

Inferring a probability distribution function for the pose of a sensor network using a mobile robot

David Meger
Dimitri Marinakis
Ioannis M. Rekleitis
Gregory Dudek

In this paper we present an approach for localizing a sensor network augmented with a mobile robot which is capable of providing inter-sensor pose estimates through its odometry measurements. We present a stochastic algorithm that samples efficiently from the probability distribution for the pose of the sensor network by employing Rao-Blackwellization and a proposal scheme which exploits the sequential nature of odometry measurements. Our algorithm automatically tunes itself to the problem instance and includes a principled stopping mechanism based on convergence analysis. We demonstrate the favourable performance of our approach compared to that of established methods via simulations and experiments on hardware.

IROS Conference 2008 Conference Paper

Enabling autonomous capabilities in underwater robotics

Junaed Sattar
Gregory Dudek
Olivia Chiu
Ioannis M. Rekleitis
Philippe Giguère
Alec Mills
Nicolas Plamondon
Chris Prahacs

Underwater operations present unique challenges and opportunities for robotic applications. These can be attributed in part to limited sensing capabilities, and to locomotion behaviours requiring control schemes adapted to specific tasks or changes in the environment. From enhancing teleoperation procedures, to providing high-level instruction, all the way to fully autonomous operations, enabling autonomous capabilities is fundamental for the successful deployment of underwater robots. This paper presents an overview of the approaches used during underwater sea trials in the coral reefs of Barbados, for two amphibious mobile robots and a set of underwater sensor nodes. We present control mechanisms used for maintaining a preset trajectory during enhanced teleoperations and discuss their experimental results. This is followed by a discussion on amphibious data gathering experiments conducted on the beach. We then present a tetherless underwater communication approach based on pure vision for high-level control of an underwater vehicle. Finally the construction details together with preliminary results from a set of distributed underwater sensor nodes are outlined.

IROS Conference 2008 Conference Paper

Heuristic search planning to reduce exploration uncertainty

David Meger
Ioannis M. Rekleitis
Gregory Dudek

The path followed by a mobile robot while mapping an environment (i. e. an exploration trajectory) plays a large role in determining the efficiency of the mapping process and the accuracy of any resulting metric map of the environment. This paper examines some important aspects of path planning in this context: the trade-offs between the speed of the exploration process versus the accuracy of resulting maps; and alternating between exploration of new territory and planning through known maps. The resulting motion planning strategy and associated heuristic are targeted to a robot building a map of an environment assisted by a Sensor Network composed of uncalibrated monocular cameras. An adaptive heuristic exploration strategy based on A * search over a combined distance and uncertainty cost function allows for adaptation to the environment and improvement in mapping accuracy. We assess the technique using an illustrative experiment in a real environment and a set of simulations in a parametric family of idealized environments.

IROS Conference 2007 Conference Paper

Over-the-horizon, autonomous navigation for planetary exploration

Ioannis M. Rekleitis
Jean-Luc Bedwani
Erick Dupuis

The success of NASA's Mars exploration rovers has demonstrated the important benefits that mobility adds to planetary exploration. Very soon, mission requirements will impose that planetary exploration rovers drive over-the-horizon in a single command cycle. This require an evolution of the methods and technologies currently used. This paper presents experimental validation of our over-the-horizon autonomous planetary navigation. We present our approach to 3D terrain reconstruction from large sparse range data sets, localization and autonomous navigation in a Mars-like terrain. Our approach is based on on-line acquisition of range scans, map construction from these scans, path planning and navigation using the map. An autonomy engine supervises the whole process ensuring the safe navigation of the planetary rover. The outdoor experimental results demonstrate the effectiveness of the reconstructed terrain model for rover localization, path planning and motion execution scenario as well as the autonomy capability of our approach.

ICRA Conference 2006 Conference Paper

Autonomous Capture of a Tumbling Satellite

Guy Rouleau
Ioannis M. Rekleitis
Régent L'Archevêque
Eric Martin
Kourosh Parsa
Erick Dupuis

In this paper, we describe a framework for the autonomous capture and servicing of satellites. The work is based on laboratory experiments that illustrate the autonomy and remote-operation aspects. The satellite-capture problem is representative of most on-orbit robotic manipulation tasks where the environment is known and structured, but it is dynamic since the satellite to be captured is in free flight. Bandwidth limitations and communication dropouts dominate the quality of the communication link. The satellite-servicing scenario is implemented on a robotic test-bed in laboratory settings

ICRA Conference 2006 Conference Paper

Distributed Coverage with Multi-robot System

Chan Sze Kong
Ai Peng New
Ioannis M. Rekleitis

In this paper, we proposed an improved algorithm for the multi-robot complete coverage problem. Real world applications such as lawn mowing, chemical spill clean-up, and humanitarian de-mining can be automated by the employment of a team of autonomous mobile robots. Our approach builds on a single robot coverage algorithm, Boustrophedon decomposition. The robots are initially distributed through space and each robot is allocated a virtually bounded area to cover. The area is decomposed into cells where each cell width is fixed. The decomposed area is represented using an adjacency graph, which is incrementally constructed and shared among all the robots. Communication between the robots is available without any restrictions. Experiments on both simulated and physical hardware demonstrated the viability of employing the algorithm to perform distributed coverage of a given unknown area with multiple robots

IROS Conference 2005 Conference Paper

A visually guided swimming robot

Gregory Dudek
Michael Jenkin
Chris Prahacs
Andrew Hogue
Junaed Sattar
Philippe Giguère
Andrew German
Hui Liu

We describe recent results obtained with AQUA, a mobile robot capable of swimming, walking and amphibious operation. Designed to rely primarily on visual sensors, the AQUA robot uses vision to navigate underwater using servo-based guidance, and also to obtain high-resolution range scans of its local environment. This paper describes some of the pragmatic and logistic obstacles encountered, and provides an overview of some of the basic capabilities of the vehicle and its associated sensors. Moreover, this paper presents the first ever amphibious transition from walking to swimming.

IROS Conference 2005 Conference Paper

Automated calibration of a camera sensor network

Ioannis M. Rekleitis
Gregory Dudek

In this paper we present a new approach for the online calibration of a camera sensor network. This is the first step towards fully exploiting the potential for collaboration between mobile robots and static sensors sharing the same network. In particular we propose an approach for extracting the 3D pose of each camera in a common reference frame, with the help of a mobile robot. The camera poses can then be used to further refine the robot pose or to perform other tracking tasks. The analytical formulation of the problem of pose recovery is presented together with experimental results of a six node sensor network in different configurations.

ICRA Conference 2004 Conference Paper

Arc Carving: Obtaining Accurate, Low Latency Maps from Ultrasonic Range Sensors

David Silver 0002
Deryck Morales
Ioannis M. Rekleitis
Brad Lisien
Howie Choset

In this paper we present a new technique for improving the azimuth resolution of ultrasonic range sensors frequently used with mobile robots. This improvement is achieved without a significant increase in the latency, or processing delay, of the system. Our approach decreases the azimuth uncertainty of a sensor reading by eliminating portions of the reading that are contradicted by subsequent readings. Our idea bears resemblance to space carving as used by the vision community, where a ray of light is used to define the boundaries of an obstacle. A sonar model similar to that commonly utilized by occupancy grids is used. Our method, termed arc carving, can be used to produce maps that are both accurate and with low enough latency for robust mobile robot navigation. Experimental results verify this approach over spaces as large as 5000 square meters.

ICRA Conference 2004 Conference Paper

Limited Communication, Multi-robot Team Based Coverage

Ioannis M. Rekleitis
Vincent Lee-Shue
Ai Peng New
Howie Choset

This paper presents an algorithm for the complete coverage of free space by a team of mobile robots. Our approach is based on a single robot coverage algorithm, which divides the target two-dimensional space into regions called cells, each of which can be covered with simple back-and-forth motions; the decomposition of free space in a collection of such cells is known as Boustrophedon decomposition. Single robot coverage is achieved by ensuring that the robot visits every cell. The new multi-robot coverage algorithm uses the same planar cell-based decomposition as the single robot approach, but provides extensions to handle how teams of robots cover a single cell and how teams are allocated among cells. This method allows planning to occur in a two-dimensional configuration space for a team of N robots. The robots operate under the restriction that communication between two robots is available only when they are within line of sight of each other.

IROS Conference 2003 Conference Paper

Analysis of multirobot localization uncertainty propagation

Stergios I. Roumeliotis
Ioannis M. Rekleitis

This paper deals with the problem of cooperative localization for the case of large groups of mobile robots. A Kalman filter estimator is implemented and tested for this purpose. The focus of this paper is to examine the effect on localization accuracy of the number N of participating robots and the accuracy of the sensors employed. More specifically, we investigate the improvement in localization accuracy per additional robot as the size of the team increases. Furthermore, we provide an analytical expression for the upper bound on the positioning uncertainty increase rate for a team of N robots as a function of N, the odometric and orientation uncertainty for each robot, and the accuracy of a robot tracker measuring relative positions between pairs of robots. The analytical results derived in this paper are validated in simulation for different test cases.

IROS Conference 2003 Conference Paper

Experiments in free-space triangulation using cooperative localization

Ioannis M. Rekleitis
Gregory Dudek
Evangelos E. Milios

This paper presents a first detailed case study of collaborative exploration of a substantial environment. We use a pair of cooperating robots to test multi-robot environment mapping algorithms based on triangulation of free space. The robots observe one another using a robot tracking sensor based on laser range sensing (LIDAR). The environment mapping itself is accomplished using sonar sensing. The results of this mapping are compared to those obtained using scanning laser range sensing and the scan matching algorithm. We show that with appropriate outlier rejection policies, the sonar-based map obtained using collaborative localization can be as good or, in fact, better than that obtained using what is typically considered to be a superior sensing technology.

IROS Conference 2003 Conference Paper

Hierarchical simultaneous localization and mapping

Brad Lisien
Deryck Morales
David Silver 0002
George Kantor
Ioannis M. Rekleitis
Howie Choset

This paper presents a novel method of combining topological and feature-based mapping strategies to create a hierarchical approach to simultaneous localization and mapping (SLAM). More than simply running both processes in parallel, we use the topological mapping procedure to organize local feature-based methods. The result is an autonomous exploration and mapping strategy that scales well to large environments and higher dimensions while confronting the issue of obstacle avoidance. We have obtained successful results of our approach in an area spanning 5000 square meters.

ICRA Conference 2003 Conference Paper

Probabilistic cooperative localization and mapping in practice

Ioannis M. Rekleitis
Gregory Dudek
Evangelos E. Milios

In this paper we present a probabilistic framework for the reduction in the uncertainty of a moving robot pose during exploration by using a second robot to assist. A Monte Carlo Simulation technique (specifically, a Particle Filter) is employed in order to model and reduce the accumulated odometric error. Furthermore, we study the requirements to obtain an accurate yet timely pose estimate. A team of two robots is employed to explore an indoor environment in this paper, although several aspects of the approach have been extended to larger groups. The concept behind our exploration strategy has been presented previously and is based on having one robot carry a sensor that acts as a "robot tracker" to estimate the position of the other robot. By suitable use of the tracker as an appropriate motion-control mechanism we can sweep areas of free space between the stationary and the moving robot and generate an accurate graph-based description of the environment. This graph is used to guide the exploration process. Complete exploration without any overlaps is guaranteed as a result of the guidance provided by the dual graph of the spatial decomposition (triangulation) of the environment. We present experimental results from indoor experiments in our laboratory and from more complex simulated experiments.

IROS Conference 2002 Conference Paper

Multi-robot cooperative localization: a study of trade-offs between efficiency and accuracy

Ioannis M. Rekleitis
Gregory Dudek
Evangelos E. Milios

This paper examines the tradeoffs between different classes of sensing strategy and motion control strategy in the context of terrain mapping with multiple robots. We consider a larger group of robots that can mutually estimate one another's position (in 2D or 3D) and uncertainty using a sample-based (particle filter) model of uncertainty. Our prior work has dealt with a pair of robots that estimate one another's position using visual tracking and coordinated motion. Here we extend these results and consider a richer set of sensing and motion options. In particular, we focus on issues related to confidence estimation for groups of more than two robots.

IROS Conference 2001 Conference Paper

Collaborative exploration for the construction of visual maps

Ioannis M. Rekleitis
Robert Sim
Gregory Dudek
Evangelos E. Milios

We examine the problem of learning a visual map of the environment while maintaining an accurate pose estimate. Our approach is based on using two robots in a simple collaborative scheme. Without outside information, as a robot collects training images, its position estimate accumulates errors, thus corrupting its knowledge of the positions from which observations are taken. We address this problem by deploying a second robot to observe the first one as it explores, thereby establishing a virtual tether, and enabling an accurate estimate of the robot's position while it constructs the map. We refer to this process as cooperative localization. The images collected during this process are assembled into a representation that allows vision-based position estimation from a single image at a later date. In addition to developing a formalism and concept, we validate our results experimentally and present quantitative results demonstrating the performance of the method in over 90 trials.

ICRA Conference 2000 Conference Paper

Multi-Robot Collaboration for Robust Exploration

Ioannis M. Rekleitis
Gregory Dudek
Evangelos E. Milios

This paper presents a new sensing modality and stratagem for multirobot exploration. The approach is based on using pairs of robots that observe each other's behavior, acting in concert to reduce odometry errors. We assume the robots can both directly sense nearby obstacles and see each other. This allows the robots to obtain a map of higher accuracy than would be possible with robots acting independently by reducing inaccuracies that occur over time from dead reckoning errors. Furthermore, by exploiting the ability of the robots to see each other, we can detect opaque obstacles in the environment independently of their surface reflectance properties. Two different algorithms, based on the size of the environment, are introduced with a complexity analysis, and experimental results in simulation and with real robots.

ICRA Conference 1999 Conference Paper

Efficient Topological Exploration

Ioannis M. Rekleitis
Vida Dujmovic
Gregory Dudek

We consider the robot exploration of a planar graph-like world. The robot's goal is to build a complete map of its environment. The environment is modeled as an arbitrary undirected planar graph which is initially unknown to the robot. The robot cannot distinguish vertices and edges that it has explored from the unexplored ones. The robot is assumed to be able to autonomously traverse graph edges, recognize when it has reached a vertex, and enumerate edges incident upon the current vertex. The robot cannot measure distances nor does it have a compass, but it is equipped with a single marker that it can leave at a vertex and sense if the marker is present at a newly visited vertex. The total number of edges traversed while constructing a map of a graph is used as a measure of performance. We present an efficient algorithm for learning an unknown, undirected planar graph by a robot equipped with one marker. Experimental results obtained by running a large collection of example worlds are presented.

IJCAI Conference 1997 Conference Paper

Multi-Robot Exploration of an Unknown Environment, Efficiently Reducing the Odometry Error

Ioannis M. Rekleitis
Gregory Dudek
Evangelos E. Milios

This paper deals with the intelligent exploration of an unknown environment by autonomous robots. In particular, we present an algorithm and associated analysis for collaborative exploration using two mobile robots. Our approach is based on robots with range sensors limited by distance. By appropriate behavioural strategies, we show that odometry (motion) errors that would normally present problems for mapping can be severely reduced. Our analysis includes polynomial complexity bounds and a discussion of possible heuristics.

ICRA Conference 1996 Conference Paper

Just-in-time sensing: efficiently combining sonar and laser range data for exploring unknown worlds

Gregory Dudek
Paul Freedman
Ioannis M. Rekleitis

This paper describes an approach to combining range data from both a set of sonar sensors as well as from a directional laser range finder to efficiently take advantage of the characteristics of both types of devices when exploring and mapping unknown worlds. The authors call their approach "just in time sensing" because it uses the more accurate but constrained laser range sensor only as needed, based upon a preliminary interpretation of sonar data. In this respect, it resembles "just in time" inventory control which attempts to judiciously obtain materials for industrial manufacturing only when and as needed. Experiments with a mobile robot equipped with sonar and a laser rangefinder demonstrate that by judiciously using the more accurate but more complex laser rangefinder to deal with the well-known ambiguity which arises in sonar data, the authors are able to obtain a much better map of an interior space at little additional cost (in terms of time and computational expense).