Author name cluster

Philippe Giguère

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.

32 papers

2 author rows

IROS Conference 2025 Conference Paper

Fast(er) Robust Point Cloud Alignment Using Lie Algebra

Jean-Thomas Sexton
Michael Morin
Philippe Giguère
Jonathan Gaudreault

We present a novel Lie algebra based Iterative Reweighted Least Squares (IRLS) algorithm for robust 3D point cloud alignment. We reformulate the optimal update computation to a compact form which requires only one pass through the data. Although this reformulation does not alter the asymptotic computational complexity, it is well suited for contemporary hardware architectures, yielding significant practical speedups. In extensive experiments on challenging benchmark datasets with added correspondence corruption, the method is consistently at least four times faster than previous literature whilst being mathematically equivalent, demonstrating it is well suited for time-critical applications.

ICRA Conference 2025 Conference Paper

UAV-Assisted Self-Supervised Terrain Awareness for Off-Road Navigation

Jean-Michel Fortin
Olivier Gamache
William Fecteau
Effie Daum
William Larrivée-Hardy
François Pomerleau
Philippe Giguère

Terrain awareness is an essential milestone to enable truly autonomous off-road navigation. Accurately predicting terrain characteristics allows optimizing a vehicle's path against potential hazards. Recent methods use deep neural networks to predict terrain properties in a self-supervised manner, relying on proprioception as a training signal. However, onboard cameras are inherently limited by their point-ofview relative to the ground, suffering from occlusions and vanishing pixel density with distance. This paper introduces a novel approach for self-supervised terrain characterization using an aerial perspective from a hovering drone. We capture terrain-aligned images while sampling the environment with a ground vehicle, effectively training a simple predictor for vibrations, bumpiness, and energy consumption. Our dataset includes 2. 8 km of off-road data collected in forest environment, comprising 13484 ground-based images and 12935 aerial images. Our findings show that drone imagery improves terrain property prediction by 21. 37% on the whole dataset and 37. 35% in high vegetation, compared to ground robot images. We conduct ablation studies to identify the main causes of these performance improvements. We also demonstrate the realworld applicability of our approach by scouting an unseen area with a drone, planning and executing an optimized path on the ground.

ICRA Conference 2024 Conference Paper

DRIVE: Data-driven Robot Input Vector Exploration

Dominic Baril
Simon-Pierre Deschênes
Luc Coupal
Cyril Goffin
Julien Lépine
Philippe Giguère
François Pomerleau

An accurate motion model is a fundamental component of most autonomous navigation systems. While much work has been done on improving model formulation, no standard protocol exists for gathering empirical data required to train models. In this work, we address this issue by proposing Data-driven Robot Input Vector Exploration (DRIVE), a protocol that enables characterizing uncrewed ground vehicles (UGVs) input limits and gathering empirical model training data. We also propose a novel learned slip approach outperforming similar acceleration learning approaches. Our contributions are validated through an extensive experimental evaluation, cumulating over 7km and 1. 8h of driving data over three distinct UGVs and four terrain types. We show that our protocol offers increased predictive performance over common human-driven data-gathering protocols. Furthermore, our protocol converges with 46 s of training data, almost four times less than the shortest human dataset gathering protocol. We show that the operational limit for our model is reached in extreme slip conditions encountered on surfaced ice. DRIVE is an efficient way of characterizing UGV motion in its operational conditions. Our code and dataset are both available online at this link: https://github.com/norlab-ulaval/DRIVE.

IROS Conference 2024 Conference Paper

Exposing the Unseen: Exposure Time Emulation for Offline Benchmarking of Vision Algorithms

Olivier Gamache
Jean-Michel Fortin
Matej Boxan
Maxime Vaidis
François Pomerleau
Philippe Giguère

Visual Odometry (VO) is one of the fundamental tasks in computer vision for robotics. However, its performance is deeply affected by High Dynamic Range (HDR) scenes, omnipresent outdoor. While new Automatic-Exposure (AE) approaches to mitigate this have appeared, their comparison in a reproducible manner is problematic. This stems from the fact that the behavior of AE depends on the environment, and it affects the image acquisition process. Consequently, AE has traditionally only been benchmarked in an online manner, making the experiments non-reproducible. To solve this, we propose a new methodology based on an emulator that can generate images at any exposure time. It leverages BorealHDR, a unique multi-exposure stereo dataset collected over 10 km, on 55 trajectories with challenging illumination conditions. Moreover, it includes lidar-inertial-based global maps with pose estimation for each image frame as well as Global Navigation Satellite System (GNSS) data, for comparison. We show that using these images acquired at different exposure times, we can emulate realistic images, keeping a Root-Mean-Square Error (RMSE) below 1. 78 % compared to ground truth images. To demonstrate the practicality of our approach for offline benchmarking, we compared three state-of-the-art AE algorithms on key elements of Visual Simultaneous Localization And Mapping (VSLAM) pipeline, against four baselines. Consequently, reproducible evaluation of AE is now possible, speeding up the development of future approaches. Our code and dataset are available on-line at this link: https://github.com/norlab-ulaval/BorealHDR

ICRA Conference 2024 Conference Paper

Log Loading Automation for Timber-Harvesting Industry

Elie Ayoub
Heshan Fernando
William Larrivée-Hardy
Nicolas Lemieux
Philippe Giguère
Inna Sharf

The timber-harvesting industry is lagging its peer industries, such as mining and agriculture, with respect to deployment of robotic, AI and autonomous technologies. In this paper, we tackle automation of a critical task that arises in transporting logs from the forest to the sawmill: the log loading operation. This work is motivated by the acute shortages of human operators and the need to improve the efficiencies of timber-harvesting processes. To this end, we demonstrate the full autonomy pipeline for the log loading operation with a fixed-base manipulator (a. k. a. , the crane), starting with perception of logs around the machine, then grasp planning for where to grasp logs, through motion planning and control of the log loading maneuver. Our main contribution is in the full integration of the necessary elements to achieve a completely autonomous loading cycle, where the crane picks up and loads all logs within its reach on a trailer. Notable features of our implementation are a generalizable perception stack, a grasp planner to pick up multiple logs at a time and an extensive experimental campaign conducted outdoors, on a commercial log loader retrofitted for autonomy. Our results demonstrate an overall 87% success rate of the log loading operation, with primary failure cases due to log segmentation errors and deficiencies in the final height adjustment algorithm for grasping logs. We also present detailed timing results of the main parts of the autonomy pipeline, which support the feasibility of deployment in operational environment.

IROS Conference 2024 Conference Paper

Proprioception Is All You Need: Terrain Classification for Boreal Forests

Damien LaRocque
William Guimont-Martin
David-Alexandre Duclos
Philippe Giguère
François Pomerleau

Recent works in field robotics highlighted the importance of resiliency against different types of terrains. Boreal forests, in particular, are home to many mobility-impeding terrains that should be considered for off-road autonomous navigation. Also, being one of the largest land biomes on Earth, boreal forests are an area where autonomous vehicles are expected to become increasingly common. In this paper, we address the issue of classifying boreal terrains by introducing BorealTC, a publicly available dataset for proprioceptive-based terrain classification (TC). Recorded with a Husky A200, our dataset contains 116 min of Inertial Measurement Unit (IMU), motor current, and wheel odometry data, focusing on typical boreal forest terrains, notably snow, ice, and silty loam. Combining our dataset with another dataset from the literature, we evaluate both a Convolutional Neural Network (CNN) and the novel state space model (SSM)-based Mamba architecture on a TC task. We show that while CNN outperforms Mamba on each separate dataset, Mamba achieves greater accuracy when trained on a combination of both. In addition, we demonstrate that Mamba’s learning capacity is greater than a CNN for increasing amounts of data. We show that the combination of two TC datasets yields a latent space that can be interpreted with the properties of the terrains. We also discuss the implications of merging datasets on classification. Our source code and dataset are publicly available online: https://github.com/norlab-ulaval/BorealTC.

ICRA Conference 2024 Conference Paper

RTS-GT: Robotic Total Stations Ground Truthing dataset

Maxime Vaidis
Mohsen Hassanzadeh Shahraji
Effie Daum
William Dubois
Philippe Giguère
François Pomerleau

Numerous datasets and benchmarks exist to assess and compare Simultaneous Localization and Mapping (SLAM) algorithms. Nevertheless, their precision must follow the rate at which SLAM algorithms improved in recent years. Moreover, current datasets fall short of comprehensive data-collection protocol for reproducibility and the evaluation of the precision or accuracy of the recorded trajectories. With this objective in mind, we proposed the Robotic Total Stations Ground Truthing dataset (RTS-GT) dataset to support localization research with the generation of six-Degrees Of Freedom (DOF) ground truth trajectories. This novel dataset includes six-DOF ground truth trajectories generated using a system of three Robotic Total Stations (RTSs) tracking moving robotic platforms. Furthermore, we compare the performance of the RTS-based system to a Global Navigation Satellite System (GNSS)-based setup. The dataset comprises around sixty experiments conducted in various conditions over a period of 17 months, and encompasses over 49 kilometers of trajectories, making it the most extensive dataset of RTS-based measurements to date. Additionally, we provide the precision of all poses for each experiment, a feature not found in the current state-of-the-art datasets. Our results demonstrate that RTSs provide measurements that are 22 times more stable than GNSS in various environmental settings, making them a valuable resource for SLAM benchmark development.

ICRA Conference 2024 Conference Paper

Saturation-Aware Angular Velocity Estimation: Extending the Robustness of SLAM to Aggressive Motions

Simon-Pierre Deschênes
Dominic Baril
Matej Boxan
Johann Laconte
Philippe Giguère
François Pomerleau

We propose a novel angular velocity estimation method to increase the robustness of Simultaneous Localization And Mapping (SLAM) algorithms against gyroscope saturations induced by aggressive motions. Field robotics expose robots to various hazards, including steep terrains, landslides, and staircases, where substantial accelerations and angular velocities can occur if the robot loses stability and tumbles. These extreme motions can saturate sensor measurements, especially gyroscopes, which are the first sensors to become inoperative. While the structural integrity of the robot is at risk, the robustness of the SLAM framework is oftentimes given little consideration. Consequently, even if the robot is physically capable of continuing the mission, its operation will be compromised due to a corrupted representation of the world. Regarding this problem, we propose a method to estimate the angular velocity using accelerometers during extreme rotations caused by tumbling. We show that our method reduces the median localization error by 71. 5 % in translation and 65. 5 % in rotation and is robust to mapping failures, which occurred in 37. 5 % of the experiments without our method. We also propose the Tumbling-Induced Gyroscope Saturation (TIGS) dataset, which consists of outdoor experiments recording the motion of a mechanical lidar subject to angular velocities four times higher than other similar datasets available. The dataset is available online at https://github.com/norlab-ulaval/Norlab_wiki/wiki/TIGS-Dataset.

IROS Conference 2023 Conference Paper

MaskBEV: Joint Object Detection and Footprint Completion for Bird's-Eye View 3D Point Clouds

William Guimont-Martin
Jean-Michel Fortin
François Pomerleau
Philippe Giguère

Recent works in object detection in LiDAR point clouds mostly focus on predicting bounding boxes around objects. This prediction is commonly achieved using anchor-based or anchor-free detectors that predict bounding boxes, requiring significant explicit prior knowledge about the objects to work properly. To remedy these limitations, we propose MaskBEV, a bird's-eye view (BEV) mask-based object detector neural architecture. MaskBEV predicts a set of BEV instance masks that represent the footprints of detected objects. Moreover, our approach allows object detection and footprint completion in a single pass. MaskBEV also reformulates the detection problem purely in terms of classification, doing away with regression usually done to predict bounding boxes. We evaluate the performance of MaskBEV on both SemanticKITTI and KITTI datasets while analyzing the architecture advantages and limitations.

IROS Conference 2022 Conference Paper

Instance Segmentation for Autonomous Log Grasping in Forestry Operations

Jean-Michel Fortin
Olivier Gamache
Vincent Grondin
François Pomerleau
Philippe Giguère

Wood logs picking is a challenging task to automate. Indeed, logs usually come in cluttered configurations, randomly orientated and overlapping. Recent work on log picking automation usually assume that the logs' pose is known, with little consideration given to the actual perception problem. In this paper, we squarely address the latter, using a data-driven approach. First, we introduce a novel dataset, named TimberSeg 1. 0, that is densely annotated, i. e. , that includes both bounding boxes and pixel-level mask annotations for logs. This dataset comprises 220 images with 2500 individually segmented logs. Using our dataset, we then compare three neural network architectures on the task of individual logs detection and segmentation; two region-based methods and one attention-based method. Unsurprisingly, our results show that axis-aligned proposals, failing to take into account the directional nature of logs, underperform with 19. 03 mAP. A rotation-aware proposal method significantly improve results to 31. 83 mAP. More interestingly, a Transformer-based approach, without any inductive bias on rotations, outperformed the two others, achieving a mAP of 57. 53 on our dataset. Our use case demonstrates the limitations of region-based approaches for cluttered, elongated objects. It also highlights the potential of attention-based methods on this specific task, as they work directly at the pixel-level. These encouraging results indicate that such a perception system could be used to assist the operators on the short-term, or to fully automate log picking operations in the future.

UAI Conference 2020 Conference Paper

The Indian Chefs Process

Patrick Dallaire
Luca Ambrogioni
Ludovic Trottier
Umut Güçlü
Max Hinne
Philippe Giguère
Marcel van Gerven
François Laviolette

This paper introduces the Indian chefs process (ICP) as a Bayesian nonparametric prior on the joint space of infinite directed acyclic graphs (DAGs) and orders that generalizes the Indian buffet process. As our construction shows, the proposed distribution relies on a latent Beta process controlling both the orders and outgoing connection probabilities of the nodes, and yields a probability distribution on sparse infinite graphs. The main advantage of the ICP over previously proposed Bayesian nonparametric priors for DAG structures is its greater flexibility. To the best of our knowledge, the ICP is the first Bayesian nonparametric model supporting every possible DAG involving latent nodes. We demonstrate the usefulness of the ICP on learning the structure of deep generative sigmoid networks as well as convolutional neural networks.

ICRA Conference 2019 Conference Paper

Analysis of Robust Functions for Registration Algorithms

Philippe Babin
Philippe Giguère
François Pomerleau

Registration accuracy is influenced by the presence of outliers and numerous robust solutions have been developed over the years to mitigate their effect. However, without a large scale comparison of solutions to filter outliers, it is becoming tedious to select an appropriate algorithm for a given application. This paper presents a comprehensive analysis of the effects of outlier filters on the Iterative Closest Point (ICP) algorithm aimed at a mobile robotic application. Fourteen of the most common outlier filters (such as M-estimators) have been tested in different types of environments, for a total of more than two million registrations. Furthermore, the influence of tuning parameters has been thoroughly explored. The experimental results show that most outlier filters have a similar performance if they are correctly tuned. Nonetheless, filters such as Var. Trim. , Cauchy, and Cauchy MAD are more stable against different environment types. Interestingly, the simple norm L 1 produces comparable accuracy, while being parameterless.

ICRA Conference 2019 Conference Paper

CELLO-3D: Estimating the Covariance of ICP in the Real World

David Landry
François Pomerleau
Philippe Giguère

The fusion of Iterative Closest Point (ICP) registrations in existing state estimation frameworks relies on an accurate estimation of their uncertainty. In this paper, we study the estimation of this uncertainty in the form of a covariance. First, we scrutinize the limitations of existing closed-form covariance estimation algorithms over 3D datasets. Then, we set out to estimate the covariance of ICP registrations through a data-driven approach, with over 5100000 registrations on 1020 pairs from real 3D point clouds. We assess our solution upon a wide spectrum of environments, ranging from structured to unstructured and indoor to outdoor. The capacity of our algorithm to predict covariances is accurately assessed, as well as the usefulness of these estimations for uncertainty estimation over trajectories. The proposed method estimates covariances better than existing closed-form solutions, and makes predictions that are consistent with observed trajectories.

IROS Conference 2019 Conference Paper

GQ-STN: Optimizing One-Shot Grasp Detection based on Robustness Classifier

Alexandre Gariépy
Jean-Christophe Ruel
Brahim Chaib-draa
Philippe Giguère

Grasping is a fundamental robotic task needed for the deployment of household robots or furthering warehouse automation. However, few approaches are able to perform grasp detection in real time (frame rate). To this effect, we present Grasp Quality Spatial Transformer Network (GQ-STN), a one-shot grasp detection network. Being based on the Spatial Transformer Network (STN), it produces not only a grasp configuration, but also directly outputs a depth image centered at this configuration. By connecting our architecture to an externally-trained grasp robustness evaluation network, we can train efficiently to satisfy a robustness metric via the backpropagation of the gradient emanating from the evaluation network. This removes the difficulty of training detection networks on sparsely annotated databases, a common issue in grasping. We further propose to use this robustness classifier to compare approaches, being more reliable than the traditional rectangle metric. Our GQ-STN is able to detect robust grasps on the depth images of the Dex-Net 2. 0 dataset with 92. 4 % accuracy in a single pass of the network. We finally demonstrate in a physical benchmark that our method can propose robust grasps more often than previous sampling-based methods, while being more than 60 times faster.

ICRA Conference 2019 Conference Paper

Learning Object Localization and 6D Pose Estimation from Simulation and Weakly Labeled Real Images

Jean-Philippe Mercier
Chaitanya Mitash
Philippe Giguère
Abdeslam Boularias

Accurate pose estimation is often a requirement for robust robotic grasping and manipulation of objects placed in cluttered, tight environments, such as a shelf with multiple objects. When deep learning approaches are employed to perform this task, they typically require a large amount of training data. However, obtaining precise 6 degrees of freedom for ground-truth can be prohibitively expensive. This work therefore proposes an architecture and a training process to solve this issue. More precisely, we present a weak object detector that enables localizing objects and estimating their 6D poses in cluttered and occluded scenes. To minimize the human labor required for annotations, the proposed detector is trained with a combination of synthetic and a few weakly annotated real images (as little as 10 images per object), for which a human provides only a list of objects present in each image (no time-consuming annotations, such as bounding boxes, segmentation masks and object poses). To close the gap between real and synthetic images, we use multiple domain classifiers trained adversarially. During the inference phase, the resulting class-specific heatmaps of the weak detector are used to guide the search of 6D poses of objects. Our proposed approach is evaluated on several publicly available datasets for pose estimation. We also evaluated our model on classification and localization in unsupervised and semi-supervised settings. The results clearly indicate that this approach could provide an efficient way toward fully automating the training process of computer vision models used in robotics.

IROS Conference 2019 Conference Paper

ReFusion: 3D Reconstruction in Dynamic Environments for RGB-D Cameras Exploiting Residuals

Emanuele Palazzolo
Jens Behley
Philipp Lottes
Philippe Giguère
Cyrill Stachniss

Mapping and localization are essential capabilities of robotic systems. Although the majority of mapping systems focus on static environments, the deployment in real-world situations requires them to handle dynamic objects. In this paper, we propose an approach for an RGB-D sensor that is able to consistently map scenes containing multiple dynamic elements. For localization and mapping, we employ an efficient direct tracking on the truncated signed distance function (TSDF) and leverage color information encoded in the TSDF to estimate the pose of the sensor. The TSDF is efficiently represented using voxel hashing, with most computations parallelized on a GPU. For detecting dynamics, we exploit the residuals obtained after an initial registration, together with the explicit modeling of free space in the model. We evaluate our approach on existing datasets, and provide a new dataset showing highly dynamic scenes. These experiments show that our approach often surpass other state-of-the-art dense SLAM methods. We make available our dataset with the ground truth for both the trajectory of the RGB-D sensor obtained by a motion capture system and the model of the static environment using a high-precision terrestrial laser scanner. Finally, we release our approach as open source code.

IROS Conference 2019 Conference Paper

SuMa++: Efficient LiDAR-based Semantic SLAM

Xieyuanli Chen
Andres Milioto
Emanuele Palazzolo
Philippe Giguère
Jens Behley
Cyrill Stachniss

Reliable and accurate localization and mapping are key components of most autonomous systems. Besides geometric information about the mapped environment, the semantics plays an important role to enable intelligent navigation behaviors. In most realistic environments, this task is particularly complicated due to dynamics caused by moving objects, which can corrupt the mapping step or derail localization. In this paper, we propose an extension of a recently published surfel-based mapping approach exploiting three-dimensional laser range scans by integrating semantic information to facilitate the mapping process. The semantic information is efficiently extracted by a fully convolutional neural network and rendered on a spherical projection of the laser range data. This computed semantic segmentation results in point-wise labels for the whole scan, allowing us to build a semantically-enriched map with labeled surfels. This semantic map enables us to reliably filter moving objects, but also improve the projective scan matching via semantic constraints. Our experimental evaluation on challenging highways sequences from KITTI dataset with very few static structures and a large amount of moving cars shows the advantage of our semantic SLAM approach in comparison to a purely geometric, state-of-the-art approach.

IROS Conference 2018 Conference Paper

Tree Species Identification from Bark Images Using Convolutional Neural Networks

Mathieu Carpentier
Philippe Giguère
Jonathan Gaudreault

Tree species identification using bark images is a challenging problem that could prove useful for many forestry related tasks. However, while the recent progress in deep learning showed impressive results on standard vision problems, a lack of datasets prevented its use on tree bark species classification. In this work, we present, and make publicly available, a novel dataset called BarkNet 1. 0 containing more than 23, 000 high-resolution bark images from 23 different tree species over a wide range of tree diameters. With it, we demonstrate the feasibility of species recognition through bark images, using deep learning. More specifically, we obtain an accuracy of 93. 88% on single crop, and an accuracy of 97. 81% using a majority voting approach on all of the images of a tree. We also empirically demonstrate that, for a fixed number of images, it is better to maximize the number of tree individuals in the training database, thus directing future data collection efforts.

IROS Conference 2016 Conference Paper

A convolutional neural network for robotic arm guidance using sEMG based frequency-features

Ulysse Côté Allard
François Nougarou
Cheikh Latyr Fall
Philippe Giguère
Clément Gosselin
François Laviolette
Benoit Gosselin

Recently, robotics has been seen as a key solution to improve the quality of life of amputees. In order to create smarter robotic prosthetic devices to be used in an everyday context, one must be able to interface them seamlessly with the end-user in an inexpensive, yet reliable way. In this paper, we are looking at guiding a robotic device by detecting gestures through measurement of the electrical activity of muscles captured by surface electromyography (sEMG). Reliable sEMG-based gesture classifiers for end-users are challenging to design, as they must be extremely robust to signal drift, muscle fatigue and small electrode displacement without the need for constant recalibration. In spite of extensive research, sophisticated sEMG classifiers for prostheses guidance are not yet widely used, as systems often fail to solve these issues simultaneously. We propose to address these problems by employing Convolutional Neural Networks. Specifically as a first step, we demonstrate their viability to the problem of gesture recognition for a low-cost, low-sampling rate (200Hz) consumer-grade, 8-channel, dry electrodes sEMG device called Myo armband (Thalmic Labs) on able-bodied subjects. To this effect, we assessed the robustness of this machine learning oriented approach by classifying a combination of 7 hand/wrist gestures with an accuracy of ∼97. 9% in real-time, over a period of 6 consecutive days with no recalibration. In addition, we used the classifier (in conjunction with orientation data) to guide a 6DoF robotic arm, using the armband with the same speed and precision as with a joystick. We also show that the classifier is able to generalize to different users by testing it on 18 participants.

ICRA Conference 2015 Conference Paper

Learning legged swimming gaits from experience

David Meger
Juan Camilo Gamboa Higuera
Anqi Xu 0003
Philippe Giguère
Gregory Dudek

We present an end-to-end framework for realizing fully automated gait learning for a complex underwater legged robot. Using this framework, we demonstrate that a hexapod flipper-propelled robot can learn task-specific control policies purely from experience data. Our method couples a state-of-the-art policy search technique with a family of periodic low-level controls that are well suited for underwater propulsion. We demonstrate the practical efficacy of tabula rasa learning, that is, learning without the use of any prior knowledge, of policies for a six-legged swimmer to carry out a variety of acrobatic maneuvers in three dimensional space. We also demonstrate informed learning that relies on simulated experience from a realistic simulator. In numerous cases, novel emergent gait behaviors have arisen from learning, such as the use of one stationary flipper to create drag while another oscillates to create thrust. Similar effective results have been demonstrated in under-actuated configurations, where as few as two flippers are used to maneuver the robot to a desired pose, or through an acrobatic motion such as a corkscrew. The success of our learning framework is assessed both in simulation and in the field using an underwater swimming robot.

IROS Conference 2015 Conference Paper

Learning terrain types with the Pitman-Yor process mixtures of Gaussians for a legged robot

Patrick Dallaire
Krzysztof Walas
Philippe Giguère
Brahim Chaib-draa

One of the major goals for mobile robots is to be able to traverse any kind of terrains. A possible way to achieve this goal is by the use of legged robots, as they have increased mobility. However, this would require them to be able to modify their gaits, based on the identification of the terrain that they are currently traversing. In this paper, we introduce a number of novel methods to address this issue of autonomous terrain classification and clustering, based on tactile data collected with a walking robot. The proposed learning methods are based on the Pitman-Yor process mixture of Gaussians, a Bayesian nonparametric prior, well-suited for density estimation. This model is initially used to learn the non-Gaussian distribution of the features produced from proprioceptive (force/torque) signals from the legs, registered during the interaction of one robot foot with a terrain. Then, we exploit its capacity on clustering and discovering structures in the data to identify terrains in the feature space. Experiments were conducted on a six-legged robot, thus demonstrating the applicability of the Pitman-Yor process mixture of Gaussians for terrain identification. In particular, we obtained a classification success rate of 82% and 51% accuracy, with our supervised learning and unsupervised learning approach respectively.

ICRA Conference 2015 Conference Paper

Multisensor placement in 3D environments via visibility estimation and derivative-free optimization

François-Michel De Rainville
Jean-Philippe Mercier
Christian Gagné 0001
Philippe Giguère
Denis Laurendeau

This paper proposes a complete system for robotic sensor placement in initially unknown arbitrary three-dimensional environments. The system uses a novel approach for computing the quality of acquisition of a mobile sensor group in such environments. The quality of acquisition is based on a geometric model of a camera which allows accurate sensor models and simple occlusion computation. The proposed system combines this new metric with a global derivative-free optimization algorithm to find simultaneously the number of sensors and their configuration to sense accordingly the environment. The presented framework compares favourably with current techniques working in two-dimensional environments. Furthermore, simulation and experimental results demonstrate the ability of the system to cope with full three-dimensional environments, a domain still unexplored by previous methods.

IROS Conference 2014 Conference Paper

3D trajectory synthesis and control for a legged swimming robot

David Meger
Florian Shkurti
David Cortés Poza
Philippe Giguère
Gregory Dudek

Inspection and exploration of complex underwater structures requires the development of agile and easy to program platforms. In this paper, we describe a system that enables the deployment of an autonomous underwater vehicle in 3D environments proximal to the ocean bottom. Unlike many previous approaches, our solution: uses oscillating hydrofoil propulsion; allows for stable control of the robot's motion and sensor directions; allows human operators to specify detailed trajectories in a natural fashion; and has been successfully demonstrated as a holistic system in the open ocean near both coral reefs and a sunken cargo ship. A key component of our system is the 3D control of a hexapod swimming robot, which can move the vehicle through agile sequences of orientations despite challenging marine conditions. We present two methods to easily generate robot trajectories appropriate for deployments in close proximity to challenging contours of the sea floor. Both offline recording of trajectories using augmented reality and online placement of fiducial tags in the marine environment are shown to have desirable properties, with complementary strengths and weaknesses. Finally, qualitative and quantitative results of the 3D control system are presented.

AAAI Conference 2014 Conference Paper

Learning the Structure of Probabilistic Graphical Models with an Extended Cascading Indian Buffet Process

Patrick Dallaire
Philippe Giguère
Brahim Chaib-draa

This paper presents an extension of the cascading Indian buffet process (CIBP) intended to learning arbitrary directed acyclic graph structures as opposed to the CIBP, which is limited to purely layered structures. The extended cascading Indian buffet process (eCIBP) essentially consists in adding an extra sampling step to the CIBP to generate connections between non-consecutive layers. In the context of graphical model structure learning, the proposed approach allows learning structures having an unbounded number of hidden random variables and automatically selecting the model complexity. We evaluated the extended process on multivariate density estimation and structure identification tasks by measuring the structure complexity and predictive performance. The results suggest the extension leads to extracting simpler graphs without scarifying predictive precision.

IJCAI Conference 2013 Conference Paper

Accelerated Robust Point Cloud Registration in Natural Environments through Positive and Unlabeled Learning

Maxime Latulippe
Alexandre Drouin
Philippe Giguère
François Laviolette

Localization of a mobile robot is crucial for autonomous navigation. Using laser scanners, this can be facilitated by the pairwise alignment of consecutive scans. In this paper, we are interested in improving this scan alignment in challenging natural environments. For this purpose, local descriptors are generally effective as they facilitate point matching. However, we show that in some natural environments, many of them are likely to be unreliable, which affects the accuracy and robustness of the results. Therefore, we propose to ﬁlter the unreliable descriptors as a prior step to alignment. Our approach uses a fast machine learning algorithm, trained on-the-ﬂy under the positive and unlabeled learning paradigm without the need for human intervention. Our results show that the number of descriptors can be signiﬁcantly reduced, while increasing the proportion of reliable ones, thus speeding up and improving the robustness of the scan alignment process.

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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.

ICRA Conference 2009 Conference Paper

Surface identification using simple contact dynamics for mobile robots

Philippe Giguère
Gregory Dudek

This paper describes an approach to surface identification in the context of mobile robotics, applicable to supervised and unsupervised learning. The identification is based on analyzing the tip acceleration patterns induced in a metallic rod, dragged along a surface that is to be identified. Eight features in time and frequency domains are used for classification. Results show that for ten type of indoor and outdoor surfaces, reliable identification can be achieved (90. 0 and 94. 6 percent for a 1 and 4 seconds time-window, respectively), using a non-sophisticated classifier (artificial neural network). Demonstration is done on how such a sensor and a simple control strategy can be used to guide a blind robot, using a simulation and a real differential drive robot.

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 2006 Conference Paper

Characterization and Modeling of Rotational Responses for an Oscillating Foil Underwater Robot

Philippe Giguère
Chris Prahacs
Gregory Dudek

In order to better understand the behavior of the underwater robot developed at our laboratory, a simple but relatively good model of the underwater behavior of the robot had to be developed. In order to be useful for model-based control techniques onboard the robot, the model had to have low computing requirements, yet be complex enough to capture the transient response of the robot. To achieve this, a system identification approach was taken by first capturing the robot response to various inputs, and then matching them to a simple model

IROS Conference 2005 Conference Paper

A visual servoing system for an aquatic swimming robot

Junaed Sattar
Philippe Giguère
Gregory Dudek
Chris Prahacs

This paper describes a visual servoing system for an underwater legged robotic system named AQUA and initial experiments with the system performed in the open sea. A large class of significant applications can be leveraged by allowing such a robot to follow a diver or some other moving target. The robot uses a suite of sensing technologies, primarily based on computer vision, to allow it to navigate in shallow-water environments. The visual servoing system described here allows the robot to track and follow a given target underwater. The servo package is made up of two distinct parts: a tracker and a feedback controller. The system has been evaluated in the sea water and under natural lighting conditions. The servo system has been tested underwater, and with minor modifications, the system can be used while the robot is walking on the ground as well.

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.