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Proprioception Is All You Need: Terrain Classification for Boreal Forests
Damien LaRocque;William Guimont-Martin;David-Alexandre Duclos;Philippe Giguère;François Pomerleau
2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
Year: 2024 | Conference Paper |
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 t...Show More
Exposing the Unseen: Exposure Time Emulation for Offline Benchmarking of Vision Algorithms
Olivier Gamache;Jean-Michel Fortin;Matěj Boxan;Maxime Vaidis;François Pomerleau;Philippe Giguère
2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
Year: 2024 | Conference Paper |
Cited by: Papers (1)
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, a...Show More
BoQ: A Place is Worth a Bag of Learnable Queries
2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Year: 2024 | Conference Paper |
Cited by: Papers (4)
In visual place recognition, accurately identifying and matching images of locations under varying environmental conditions and viewpoints remains a significant challenge. In this paper, we introduce a new technique, called Bag-of-Queries (BoQ), which learns a set of global queries, designed to capture universal place-specific attributes. Unlike existing techniques that employ self-attention and g...Show More
Log Loading Automation for Timber-Harvesting Industry
Elie Ayoub;Heshan Fernando;William Larrivée-Hardy;Nicolas Lemieux;Philippe Giguère;Inna Sharf
2024 IEEE International Conference on Robotics and Automation (ICRA)
Year: 2024 | Conference Paper |
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 improv...Show More
RTS-GT: Robotic Total Stations Ground Truthing dataset
Maxime Vaidis;Mohsen Hassanzadeh Shahraji;Effie Daum;William Dubois;Philippe Giguère;François Pomerleau
2024 IEEE International Conference on Robotics and Automation (ICRA)
Year: 2024 | Conference Paper |
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. Wi...Show More
Saturation-Aware Angular Velocity Estimation: Extending the Robustness of SLAM to Aggressive Motions*
Simon-Pierre Deschênes;Dominic Baril;Matěj Boxan;Johann Laconte;Philippe Giguère;François Pomerleau
2024 IEEE International Conference on Robotics and Automation (ICRA)
Year: 2024 | Conference Paper |
Cited by: Papers (1)
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 an...Show More
DRIVE: Data-driven Robot Input Vector Exploration
Dominic Baril;Simon-Pierre Deschênes;Luc Coupal;Cyril Goffin;Julien Lépine;Philippe Giguère;François Pomerleau
2024 IEEE International Conference on Robotics and Automation (ICRA)
Year: 2024 | Conference Paper |
Cited by: Papers (1)
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...Show More
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
2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
Year: 2023 | Conference Paper |
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 det...Show More
MixVPR: Feature Mixing for Visual Place Recognition
2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)
Year: 2023 | Conference Paper |
Cited by: Papers (87)
Visual Place Recognition (VPR) is a crucial part of mobile robotics and autonomous driving as well as other computer vision tasks. It refers to the process of identifying a place depicted in a query image using only computer vision. At large scale, repetitive structures, weather and illumination changes pose a real challenge, as appearances can drastically change over time. Along with tackling the...Show More
Kilometer-Scale Autonomous Navigation in Subarctic Forests: Challenges and Lessons Learned
Dominic Baril;Simon-Pierre Deschênes;Olivier Gamache;Maxime Vaidis;Damien LaRocque;Johann Laconte;Vladimír Kubelka;Philippe Giguère;François Pomerleau
Challenges inherent to autonomous wintertime navigation in forests include lack of a reliable Global Navigation Satellite System (GNSS) signal, low feature contrast, high illumination variations, and changing environment. This type of off-road environment is an extreme case of situations autonomous cars could encounter in northern regions. Thus, it is important to understand the impact of this har...Show More
Instance Segmentation for Autonomous Log Grasping in Forestry Operations
Jean-Michel Fortin;Olivier Gamache;Vincent Grondin;François Pomerleau;Philippe Giguère
2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
Year: 2022 | Conference Paper |
Cited by: Papers (11)
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 d...Show More
Deep Neural Network Training Using Synthetic Signatures for Rare Target Detection in SWIR Hyperspectral Imagery
2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS
Year: 2021 | Conference Paper |
Cited by: Papers (4)
Deep learning techniques have enjoyed tremendous success in the vision community, where large labeled datasets are available for training. This has been problematic for hyperspectral target detection applications, as labeled hyperspectral datasets are scarce, particularly for detection applications involving rarely occurring targets. In this work, we present a deep neural network architecture for ...Show More
Accurate outdoor ground truth based on total stations
Maxime Vaidis;Philippe Giguère;François Pomerleau;Vladimír Kubelka
In robotics, accurate ground-truth position fostered the development of mapping and localization algorithms through the creation of cornerstone datasets. In outdoor environments and over long distances, total stations are the most accurate and precise measurement instruments for this purpose. Most total station-based systems in the literature are limited to three Degrees Of Freedoms (DOFs), due to...Show More
Lidar Scan Registration Robust to Extreme Motions
Simon-Pierre Deschênes;Dominic Baril;Vladimír Kubelka;Philippe Giguère;François Pomerleau
Registration algorithms, such as Iterative Closest Point (ICP), have proven effective in mobile robot localization algorithms over the last decades. However, they are susceptible to failure when a robot sustains extreme velocities and accelerations. For example, this kind of motion can happen after a collision, causing a point cloud to be heavily skewed. While point cloud de-skewing methods have b...Show More
Deep Template-based Object Instance Detection
Jean-Philippe Mercier;Mathieu Garon;Philippe Giguère;Jean-François Lalonde
2021 IEEE Winter Conference on Applications of Computer Vision (WACV)
Year: 2021 | Conference Paper |
Cited by: Papers (12)
Much of the focus in the object detection literature has been on the problem of identifying the bounding box of a particular class of object in an image. Yet, in contexts such as robotics and augmented reality, it is often necessary to find a specific object instance-a unique toy or a custom industrial part for example-rather than a generic object class. Here, applications can require a rapid shif...Show More
Evaluation of Skid-Steering Kinematic Models for Subarctic Environments
Dominic Baril;Vincent Grondin;Simon-Pierre Deschênes;Johann Laconte;Maxime Vaidis;Vladimír Kubelka;André Gallant;Philippe Giguère;François Pomerleau
2020 17th Conference on Computer and Robot Vision (CRV)
Year: 2020 | Conference Paper |
Cited by: Papers (13)
In subarctic and arctic areas, large and heavy skid-steered robots are preferred for their robustness and ability to operate on difficult terrain. State estimation, motion control and path planning for these robots rely on accurate odometry models based on wheel velocities. However, the state-of-the-art odometry models for skid-steer mobile robots (SSMRs) have usually been tested on relatively lig...Show More
Tree bark re-identification using a deep-learning feature descriptor
2020 17th Conference on Computer and Robot Vision (CRV)
Year: 2020 | Conference Paper |
Cited by: Papers (8)
The ability to visually re-identify objects is a fundamental capability in vision systems. Oftentimes, it relies on collections of visual signatures based on descriptors, such as SIFT or SURF. However, these traditional descriptors were designed for a certain domain of surface appearances and geometries (limited relief). Consequently, highly-textured surfaces such as tree bark pose a challenge to ...Show More
ReFusion: 3D Reconstruction in Dynamic Environments for RGB-D Cameras Exploiting Residuals
Emanuele Palazzolo;Jens Behley;Philipp Lottes;Philippe Giguère;Cyrill Stachniss
2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
Year: 2019 | Conference Paper |
Cited by: Papers (118)
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 ...Show More
SuMa++: Efficient LiDAR-based Semantic SLAM
Xieyuanli Chen;Andres Milioto;Emanuele Palazzolo;Philippe Giguère;Jens Behley;Cyrill Stachniss
2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
Year: 2019 | Conference Paper |
Cited by: Papers (340)
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 localiz...Show More
GQ-STN: Optimizing One-Shot Grasp Detection based on Robustness Classifier
Alexandre Gariépy;Jean-Christophe Ruel;Brahim Chaib-draa;Philippe Giguère
2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
Year: 2019 | Conference Paper |
Cited by: Papers (11)
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 ...Show More
Analysis of Robust Functions for Registration Algorithms
2019 International Conference on Robotics and Automation (ICRA)
Year: 2019 | Conference Paper |
Cited by: Papers (35)
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 I...Show More
CELLO-3D: Estimating the Covariance of ICP in the Real World
2019 International Conference on Robotics and Automation (ICRA)
Year: 2019 | Conference Paper |
Cited by: Papers (35)
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 ...Show More
Learning Object Localization and 6D Pose Estimation from Simulation and Weakly Labeled Real Images
Jean-Philippe Mercier;Chaitanya Mitash;Philippe Giguère;Abdeslam Boularias
2019 International Conference on Robotics and Automation (ICRA)
Year: 2019 | Conference Paper |
Cited by: Papers (10)
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...Show More
Tree Species Identification from Bark Images Using Convolutional Neural Networks
2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
Year: 2018 | Conference Paper |
Cited by: Papers (42)
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 c...Show More
Convolutional Residual Network for Grasp Localization
2017 14th Conference on Computer and Robot Vision (CRV)
Year: 2017 | Conference Paper |
Cited by: Papers (3)
Object grasping is an important ability for carrying out complex manipulation tasks with autonomous robotic systems. The grasp localization module plays an essential role in the success of the grasp maneuver. Generally viewed as a vision perception problem, its goal is determining regions of high graspability by interpreting light and depth information. Over the past few years, several works in De...Show More
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