41 citations · 41 across the 16 of their papers we have counts for
16 papers
Towards Robot Skill Learning and Adaptation with Gaussian Processes
A K M Nadimul Haque, Fouad Sukkar, Sheila Sutjipto +4
General robot skill adaptation requires expressive representations robust to varying task configurations. While recent learning-based skill adaptation methods refined via Reinforce…
Revisiting Continuous-Time Trajectory Estimation via Gaussian Processes and the Magnus Expansion
Timothy Barfoot, Cedric Le Gentil, Sven Lilge
Continuous-time state estimation has been shown to be an effective means of (i) handling asynchronous and high-rate measurements, (ii) introducing smoothness to the estimate, (iii)…
Mag-Match: Magnetic Vector Field Features for Map Matching and Registration
William McDonald, Cedric Le Gentil, Jennifer Wakulicz +1
Map matching and registration are essential tasks in robotics for localisation and integration of multi-session or multi-robot data. Traditional methods rely on cameras or LiDARs t…
Towards Efficient Occupancy Mapping via Gaussian Process Latent Field Shaping
Cedric Le Gentil, Cedric Pradalier, Timothy D. Barfoot
Occupancy mapping has been a key enabler of mobile robotics. Originally based on a discrete grid representation, occupancy mapping has evolved towards continuous representations th…
Do We Still Need to Work on Odometry for Autonomous Driving?
Cedric Le Gentil, Daniil Lisus, Timothy D. Barfoot
Over the past decades, a tremendous amount of work has addressed the topic of ego-motion estimation of moving platforms based on various proprioceptive and exteroceptive sensors. A…
Gaussian Process Distance Fields Obstacle and Ground Constraints for Safe Navigation
Monisha Mushtary Uttsha, Cedric Le Gentil, Lan Wu +1
Navigating cluttered environments is a challenging task for any mobile system. Existing approaches for ground-based mobile systems primarily focus on small wheeled robots, which fa…
Real-Time Truly-Coupled Lidar-Inertial Motion Correction and Spatiotemporal Dynamic Object Detection
Cedric Le Gentil, Raphael Falque, Teresa Vidal-Calleja
Over the past decade, lidars have become a cornerstone of robotics state estimation and perception thanks to their ability to provide accurate geometric information about their sur…
Mixing Data-driven and Geometric Models for Satellite Docking Port State Estimation using an RGB or Event Camera
Cedric Le Gentil, Jack Naylor, Nuwan Munasinghe +5
In-orbit automated servicing is a promising path towards lowering the cost of satellite operations and reducing the amount of orbital debris. For this purpose, we present a pipelin…
Towards Robust Perception for Assistive Robotics: An RGB-Event-LiDAR Dataset and Multi-Modal Detection Pipeline
Adam Scicluna, Cedric Le Gentil, Sheila Sutjipto +1
The increasing adoption of human-robot interaction presents opportunities for technology to positively impact lives, particularly those with visual impairments, through application…
Safe Bubble Cover for Motion Planning on Distance Fields
Ki Myung Brian Lee, Zhirui Dai, Cedric Le Gentil +3
We consider the problem of planning collision-free trajectories on distance fields. Our key observation is that querying a distance field at one configuration reveals a region of s…
VDB-GPDF: Online Gaussian Process Distance Field with VDB Structure
Lan Wu, Cedric Le Gentil, Teresa Vidal-Calleja
Robots reason about the environment through dedicated representations. Popular choices for dense representations exploit Truncated Signed Distance Functions (TSDF) and Octree data…
Continuous-Time Gaussian Process Motion-Compensation for Event-vision Pattern Tracking with Distance Fields
Cedric Le Gentil, Ignacio Alzugaray, Teresa Vidal-Calleja
This work addresses the issue of motion compensation and pattern tracking in event camera data. An event camera generates asynchronous streams of events triggered independently by…
Accurate Gaussian-Process-based Distance Fields with applications to Echolocation and Mapping
Cedric Le Gentil, Othmane-Latif Ouabi, Lan Wu +2
This paper introduces a novel method to estimate distance fields from noisy point clouds using Gaussian Process (GP) regression. Distance fields, or distance functions, gained popu…
GPGM-SLAM: a Robust SLAM System for Unstructured Planetary Environments with Gaussian Process Gradient Maps
Riccardo Giubilato, Cedric Le Gentil, Mallikarjuna Vayugundla +3
Simultaneous Localization and Mapping (SLAM) techniques play a key role towards long-term autonomy of mobile robots due to the ability to correct localization errors and produce co…
Gaussian Process Gradient Maps for Loop-Closure Detection in Unstructured Planetary Environments
Cedric Le Gentil, Mallikarjuna Vayugundla, Riccardo Giubilato +3
The ability to recognize previously mapped locations is an essential feature for autonomous systems. Unstructured planetary-like environments pose a major challenge to these system…
IDOL: A Framework for IMU-DVS Odometry using Lines
Cedric Le Gentil, Florian Tschopp, Ignacio Alzugaray +3
In this paper, we introduce IDOL, an optimization-based framework for IMU-DVS Odometry using Lines. Event cameras, also called Dynamic Vision Sensors (DVSs), generate highly asynch…