Author name cluster

Supun Samarasekera

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.

10 papers

1 author row

IROS Conference 2022 Conference Paper

Ranging-Aided Ground Robot Navigation Using UWB Nodes at Unknown Locations

Abhinav Rajvanshi
Han-Pang Chiu
Alex Krasner
Mikhail Sizintsev
Glenn Murray
Supun Samarasekera

Ranging information from ultra-wideband (UWB) ranging radios can be used to improve estimated navigation accuracy of a ground robot with other on-board sensors. However, all ranging-aided navigation methods demand the locations of ranging nodes to be known, which is not suitable for time-pressed situations, dynamic cluttered environments, or collaborative navigation applications. This paper describes a new ranging-aided navigation approach that does not require the locations of ranging radios. Our approach formulates relative pose constraints using ranging readings. The formulation is based on geometric relationships between each stationary ranging node and two ranging antennas on the moving robot across time. Our experiments show that estimated navigation accuracy of the ground robot is substantially enhanced with ranging information using our approach under a variety of scenarios, when ranging nodes are placed at unknown locations. We analyze and compare our performance with a traditional ranging-aided method, which requires mapping the positions of ranging nodes. We also demonstrate the applicability of our approach for collaborative navigation in large-scale unknown environments, by using ranging information from one mobile robot to improve navigation estimation of the other robot. This application does not require the installation of ranging nodes at fixed locations.

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ICRA Conference 2022 Conference Paper

Towards Safe, Realistic Testbed for Robotic Systems with Human Interaction

Bhoram Lee
Jonathan Brookshire
Rhys Yahata
Supun Samarasekera

Simulation has been a necessary, safe testbed for robotics systems (RS). However, testing in simulation alone is not enough for robotic systems operating in close proximity, or interacting directly with, humans, because simulated humans are very limited. Furthermore, testing with real humans can be unsafe and costly. As recent advances in machine learning are being brought to physical robotic systems, how to collect data as well as evaluate them with human interactions safely yet realistically is a critical question. This paper presents a Mixed-Reality (MR) system toward human-centered development of robotic systems emphasizing benefits as a data collection and testbed tool. MR testbeds allow humans to interact with various levels of virtuality to maintain both realism and safety. We detail the advantages and limitations of these different levels of realism or virtualization, and report our MR-based RS testbed implemented using off-the-shelf MR devices with the Unity game engine and ROS. We demonstrate our testbed in a multi-robot, multi-person tracking and monitoring application. We share our vision and insights earned during the development and data collection.

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ICRA Conference 2021 Conference Paper

MaAST: Map Attention with Semantic Transformers for Efficient Visual Navigation

Zachary Seymour
Kowshik Thopalli
Niluthpol Chowdhury Mithun
Han-Pang Chiu
Supun Samarasekera
Rakesh Kumar 0001

Visual navigation for autonomous agents is a core task in the fields of computer vision and robotics. Learning-based methods, such as deep reinforcement learning, have the potential to outperform the classical solutions developed for this task; however, they come at a significantly increased computational load. Through this work, we design a novel approach that focuses on performing better or comparable to the existing learning-based solutions but under a clear time/computational budget. To this end, we propose a method to encode vital scene semantics such as traversable paths, unexplored areas, and observed scene objects–alongside raw visual streams such as RGB, depth, and semantic segmentation masks—into a semantically informed, top-down egocentric map representation. Further, to enable the effective use of this information, we introduce a novel 2-D map attention mechanism, based on the successful multi-layer Transformer networks. We conduct experiments on 3-D reconstructed indoor PointGoal visual navigation and demonstrate the effectiveness of our approach. We show that by using our novel attention schema and auxiliary rewards to better utilize scene semantics, we outperform multiple baselines trained with only raw inputs or implicit semantic information while operating with an 80% decrease in the agent’s experience.

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ICRA Conference 2014 Conference Paper

Constrained optimal selection for multi-sensor robot navigation using plug-and-play factor graphs

Han-Pang Chiu
Xun S. Zhou
Luca Carlone
Frank Dellaert
Supun Samarasekera
Rakesh Kumar 0001

This paper proposes a real-time navigation approach that is able to integrate many sensor types while fulfilling performance needs and system constraints. Our approach uses a plug-and-play factor graph framework, which extends factor graph formulation to encode sensor measurements with different frequencies, latencies, and noise distributions. It provides a flexible foundation for plug-and-play sensing, and can incorporate new evolving sensors. A novel constrained optimal selection mechanism is presented to identify the optimal subset of active sensors to use, during initialization and when any sensor condition changes. This mechanism constructs candidate subsets of sensors based on heuristic rules and a ternary tree expansion algorithm. It quickly decides the optimal subset among candidates by maximizing observability coverage on state variables, while satisfying resource constraints and accuracy demands. Experimental results demonstrate that our approach selects subsets of sensors to provide satisfactory navigation solutions under various conditions, on large-scale real data sets using many sensors.

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IROS Conference 2014 Conference Paper

Precise vision-aided aerial navigation

Han-Pang Chiu
Aveek Das
Phillip Miller
Supun Samarasekera
Rakesh Kumar 0001

This paper proposes a novel vision-aided navigation approach that continuously estimates precise 3D absolute pose for aerial vehicles, using only inertial measurements and monocular camera observations. Our approach is able to provide accurate navigation solutions under long-term GPS outage, by tightly incorporating absolute geo-registered information into two kinds of visual measurements: 2D-3D tie-points, and geo-registered feature tracks. 2D-3D tie-points are established by finding feature correspondences to align an aerial video frame to a 2D geo-referenced image rendered from the 3D terrain database. These measurements provide global information to correct accumulated error in navigation estimation. Geo-registered feature tracks are generated by associating features across consecutive frames. They enable the propagation of 3D geo-referenced values to further improve the pose estimation. All sensor measurements are fully optimized in a smoother-based inference framework, which achieves efficient relinearization and real-time estimation of navigation states and their covariances over a constant-length of sliding window. Experimental results demonstrate that our approach provides accurate and consistent aerial navigation solutions on several large-scale GPS-denied scenarios.

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ICRA Conference 2013 Conference Paper

Robust vision-aided navigation using Sliding-Window Factor graphs

Han-Pang Chiu
Stephen Williams
Frank Dellaert
Supun Samarasekera
Rakesh Kumar 0001

This paper proposes a navigation algorithm that provides a low-latency solution while estimating the full nonlinear navigation state. Our approach uses Sliding-Window Factor Graphs, which extend existing incremental smoothing methods to operate on the subset of measurements and states that exist inside a sliding time window. We split the estimation into a fast short-term smoother, a slower but fully global smoother, and a shared map of 3D landmarks. A novel three-stage visual feature model is presented that takes advantage of both smoothers to optimize the 3D landmark map, while minimizing the computation required for processing tracked features in the short-term smoother. This three-stage model is formulated based on the maturity of the estimation of the 3D location of the underlying landmark in the map. Long-range associations are used as global measurements from matured landmarks in the short-term smoother and loop closure constraints in the long-term smoother. Experimental results demonstrate our approach provides highly-accurate solutions on large-scale real data sets using multiple sensors in GPS-denied settings.

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IROS Conference 2012 Conference Paper

Long-Range Pedestrian Detection using stereo and a cascade of convolutional network classifiers

Zsolt Kira
Raia Hadsell
Garbis Salgian
Supun Samarasekera

In this paper, we present a system for detecting pedestrians at long ranges using a combination of stereo-based detection, classification using deep learning, and a cascade of specialized classifiers that can reduce false positives and computational load. Specifically, we use stereo to perform detection of vertical structures which are further filtered based on edge responses. A convolutional neural network was then designed to support the classification of pedestrians using both appearance and stereo disparity-based features. A second convolutional network classifier was trained specifically for the case of long-range detections using appearance only. We further speed up the classifier using a cascade approach and multi-threading. The system was deployed on two robots, one using a high resolution stereo pair with 180 degree fisheye lenses and the other using 80 degree FOV lenses. Results are demonstrated on a large dataset captured in a variety of environments.

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IROS Conference 2011 Conference Paper

A graph traversal based algorithm for obstacle detection using lidar or stereo

Sujit Kuthirummal
Aveek Das
Supun Samarasekera

We present a novel computationally efficient approach to obstacle detection that is applicable to both structured (e. g. indoor, road) and unstructured (e. g. off-road, grassy terrain) environments. In contrast to previous works that attempt to explicitly identify obstacles, we explicitly detect scene regions that are traversable - safe for the robot to go to - from its current position. Traversability is defined on a 2D grid of cells. Given 3D points, we map them to individual cells and compute histograms of elevations of the points in each cell. This elevation information is then used in a graph based algorithm to label all traversable cells. In this manner, positive and negative obstacles, as well as unknown regions are implicitly detected and avoided. Our notion of traversability does not make any flat-world assumptions and does not need sensor pitch-roll compensation. It also accounts for overhanging structures like tree branches. We demonstrate that our approach can be used with both lidar and stereo sensors even though the two sensors differ in their resolution and accuracy. We present several results from our real-time implementation on realistic environments using both lidar and stereo.

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IROS Conference 2010 Conference Paper

Multi-modal sensor fusion algorithm for ubiquitous infrastructure-free localization in vision-impaired environments

Taragay Oskiper
Han-Pang Chiu
Zhiwei Zhu
Supun Samarasekera
Rakesh Kumar 0001

In this paper, we present a unified approach for a camera tracking system based on an error-state Kalman filter algorithm. The filter uses relative (local) measurements obtained from image based motion estimation through visual odometry, as well as global measurements produced by landmark matching through a pre-built visual landmark database and range measurements obtained from radio frequency (RF) ranging radios. We show our results by using the camera poses output by our system to render views from a 3D graphical model built upon the same coordinate frame as the landmark database which also forms the global coordinate system and compare them to the actual video images. These results help demonstrate both the long term stability and the overall accuracy of our algorithm as intended to provide a solution to the GPS denied ubiquitous camera tracking problem under both vision-aided and vision-impaired conditions.

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ICRA Conference 2010 Conference Paper

Robust visual path following for heterogeneous mobile platforms

Aveek Das
Oleg Naroditsky
Zhiwei Zhu
Supun Samarasekera
Rakesh Kumar 0001

We present an innovative path following system based upon multi-camera visual odometry and visual landmark matching. This technology enables reliable mobile robot navigation in real world scenarios including GPS-denied environments both indoors and outdoors. We recover paths in full 3D, making it applicable to both on and off-road ground vehicles. Our controller relies on pose updates from visual odometry, allowing us to achieve path following even when only a joystick drive interface to the base robot platform is available. We experimentally investigate two specific applications of our technology to autonomous navigation on ground vehicles - non line-of-sight leader-following (between heterogeneous platforms) and retro-traverse to home base. For safety and reliability we add dynamic short range obstacle detection and reactive avoidance capabilities to our controller. We show the results for end-to-end real time implementation of this technology using current off-the-shelf computing and network resources in challenging environments.

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