57 citations · 147 across the 16 of their papers we have counts for
21 papers
Tree Detection and Diameter Estimation Based on Deep Learning
Vincent Grondin, Jean-Michel Fortin, François Pomerleau +1
Tree perception is an essential building block toward autonomous forestry operations. Current developments generally consider input data from lidar sensors to solve forest navigati…
Training Deep Learning Algorithms on Synthetic Forest Images for Tree Detection
Vincent Grondin, François Pomerleau, Philippe Giguère
Vision-based segmentation in forested environments is a key functionality for autonomous forestry operations such as tree felling and forwarding. Deep learning algorithms demonstra…
Extrinsic calibration for highly accurate trajectories reconstruction
Maxime Vaidis, William Dubois, Alexandre Guénette +3
In the context of robotics, accurate ground-truth positioning is the cornerstone for the development of mapping and localization algorithms. In outdoor environments and over long d…
Robotics in Snow and Ice
François Pomerleau
Definition: The terms "robotics in snow and ice" refers to robotic systems being studied, developed, and used in areas where water can be found in its solid state. This specialized…
Gravity-constrained point cloud registration
Vladimír Kubelka, Maxime Vaidis, François Pomerleau
Visual and lidar Simultaneous Localization and Mapping (SLAM) algorithms benefit from the Inertial Measurement Unit (IMU) modality. The high-rate inertial data complement the other…
Instance Segmentation for Autonomous Log Grasping in Forestry Operations
Jean-Michel Fortin, Olivier Gamache, Vincent Grondin +2
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 automati…
System for multi-robotic exploration of underground environments CTU-CRAS-NORLAB in the DARPA Subterranean Challenge
Tomáš Rouček, Martin Pecka, Petr Čížek +19
We present a field report of CTU-CRAS-NORLAB team from the Subterranean Challenge (SubT) organised by the Defense Advanced Research Projects Agency (DARPA). The contest seeks to ad…
Lidar Scan Registration Robust to Extreme Motions
Simon-Pierre Deschênes, Dominic Baril, Vladimír Kubelka +2
Registration algorithms, such as Iterative Closest Point (ICP), have proven effective in mobile robot localization algorithms over the last decades. However, they are susceptible t…
Accurate outdoor ground truth based on total stations
Maxime Vaidis, Philippe Giguère, François Pomerleau +1
In robotics, accurate ground-truth position fostered the development of mapping and localization algorithms through the creation of cornerstone datasets. In outdoor environments an…
Dynamic Lambda-Field: A Counterpart of the Bayesian Occupancy Grid for Risk Assessment in Dynamic Environments
Johann Laconte, Elie Randriamiarintsoa, Abderrahim Kasmi +4
In the context of autonomous vehicles, one of the most crucial tasks is to estimate the risk of the undertaken action. While navigating in complex urban environments, the Bayesian…
A Novel Occupancy Mapping Framework for Risk-Aware Path Planning in Unstructured Environments
Johann Laconte, Abderrahim Kasmi, François Pomerleau +4
In the context of autonomous robots, one of the most important tasks is to prevent potential damage to the robot during navigation. For this purpose, it is often assumed that one m…
Evaluation of Skid-Steering Kinematic Models for Subarctic Environments
Dominic Baril, Vincent Grondin, Simon-Pierre Deschênes +6
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…
Predicting GNSS satellite visibility from dense point clouds
Philippe Dandurand, Philippe Babin, Vladimır Kubelka +2
To help future mobile agents plan their movement in harsh environments,a predictive model has been designed to determine what areas would be favorable for Global Navigation Satelli…
Large-scale 3D Mapping of Subarctic Forests
Philippe Babin, Philippe Dandurand, Vladimír Kubelka +2
The ability to map challenging subarctic environments opens new horizons for robotic deployments in industries such as forestry, surveillance, and open-pit mining. In this paper, w…
Automatic 3D Mapping for Tree Diameter Measurements in Inventory Operations
Jean-François Tremblay, Martin Béland, François Pomerleau +2
Forestry is a major industry in many parts of the world. It relies on forest inventory, which consists of measuring tree attributes. We propose to use 3D mapping, based on the iter…
Lambda-Field: A Continuous Counterpart of the Bayesian Occupancy Grid for Risk Assessment
Johann Laconte, Christophe Debain, Roland Chapuis +2
In a context of autonomous robots, one of the most important task is to ensure the safety of the robot and its surrounding. Most of the time, the risk of navigation is simply said…
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…
CELLO-3D: Estimating the Covariance of ICP in the Real World
David Landry, François Pomerleau, Philippe Giguère
The fusion of Iterative Closest Point (ICP) reg- istrations in existing state estimation frameworks relies on an accurate estimation of their uncertainty. In this paper, we study t…
Geometry Preserving Sampling Method based on Spectral Decomposition for 3D Registration
Mathieu Labussiere, Johann Laconte, François Pomerleau
In the context of 3D mapping, larger and larger point clouds are acquired with LIDAR sensors. The Iterative Closest Point (ICP) algorithm is used to align these point clouds. Howev…
Lidar Measurement Bias Estimation via Return Waveform Modelling in a Context of 3D Mapping
Johann Laconte, Simon-Pierre Deschênes, Mathieu Labussière +1
In a context of 3D mapping, it is very important to get accurate measurements from sensors. In particular, Light Detection And Ranging (LIDAR) measurements are typically treated as…
Learning a Bias Correction for Lidar-only Motion Estimation
Tim Y. Tang, David J. Yoon, François Pomerleau +1
This paper presents a novel technique to correct for bias in a classical estimator using a learning approach. We apply a learned bias correction to a lidar-only motion estimation p…