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Cedric Le Gentil

22 accepted papers

2026

BIEVR-LIO: Robust LiDAR-Inertial Odometry through Bump-Image-Enhanced Voxel Maps

Patrick Pfreundschuh, Turcan Tuna, Cedric Le Gentil, Roland Siegwart, Cesar Cadena, Helen Oleynikova
RSS 2026poster

Reliable odometry is essential for mobile robots as they increasingly enter more challenging environments, which often contain little information to constrain point cloud registration, resulting in degraded LiDAR–Inertial Odometry (LIO) accuracy or even divergence. To address this, we present BIEVR-…

Cited by 0Source ↗Scholar ↗
2026

Breaking the Static Assumption: A Dynamic-Aware LIO Framework Via Spatio-Temporal Normal Analysis

Zhiqiang Chen, Cedric Le Gentil, Fuling Lin, Minghao Lu, Qiyuan Qiao, Bowen Xu +2
ICRA 2026poster

This paper addresses the challenge of Lidar-Inertial Odometry (LIO) in dynamic environments, where conventional methods often fail due to their static-world assumptions. Traditional LIO algorithms perform poorly when dynamic objects dominate the scenes, particularly in geometrically sparse environme…

Cited by 0Source ↗PDF ↗code ↗Scholar ↗
2026

Dr-BA: Separable Optimization for Direct Radar Bundle Adjustment & Localization

Daniil Lisus, Cedric Le Gentil, Timothy Barfoot
RSS 2026poster

This paper introduces Dr-BA, a first-of-its-kind radar bundle adjustment (BA) framework that operates directly on 2D spinning radar intensity images. Unlike camera or lidar sensors, radar is largely unaffected by precipitation, making it a critical modality for autonomous systems that require all-we…

Cited by 0Source ↗Scholar ↗
2026

Towards Robot Skill Learning and Adaptation With Gaussian Processes

A. K. M. Nadimul Haque, Fouad Sukkar, Sheila Sutjipto, Cedric Le Gentil, Marc G. Carmichael, Teresa Vidal-Calleja
RA-L 2026

General robot skill adaptation requires expressive representations robust to varying task configurations. While recent learning-based skill adaptation methods refined via Reinforcement learning (RL) have shown success, existing skill models often lack sufficient representational capacity for anythin

Cited by 0Source ↗Scholar ↗
2025

DRO: Doppler-Aware Direct Radar Odometry with Gyroscope

Cedric Le Gentil, Leonardo Brizi, Daniil Lisus, Xinyuan Qiao, Giorgio Grisetti, Timothy Barfoot
RSS 2025poster

A renaissance in radar-based sensing for mobile robotic applications is underway. Compared to cameras or lidars, millimetre-wave radars have the ability to `see’ through thin walls, vegetation, and adversarial weather conditions such as heavy rain, fog, snow, and dust. In this paper, we propose a no…

Cited by 0PDF ↗Scholar ↗
2025

Mag-Match: Magnetic Vector Field Features for Map Matching and Registration

William McDonald, Cedric Le Gentil, Jennifer Wakulicz, Teresa A. Vidal-Calleja
IROS 2025

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 to capture visual or geometric information but struggle in challenging conditions like smoke or dust. Magnetometers, on t

Cited by 0Source ↗Scholar ↗
2025

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, Jasprabhjit Mehami, Benny Dai, Mikhail Asavkin +2
ICRA 2025

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 pipeline for automated satellite docking port detection and state estimation using monocular vision data from standard RGB sens

Cited by 2Source ↗Scholar ↗
2024

Accurate Gaussian-Process-Based Distance Fields With Applications to Echolocation and Mapping

Cedric Le Gentil, Othmane-Latif Ouabi, Lan Wu, Cédric Pradalier, Teresa A. Vidal-Calleja
RA-L 2024

This letter introduces a novel method to estimate distance fields from noisy point clouds using Gaussian Process (GP) regression. Distance fields, or distance functions, gained popularity for applications like point cloud registration, odometry, SLAM, path planning, shape reconstruction, etc. A dist

Cited by 26Source ↗Scholar ↗
2024

RATE: Real-time Asynchronous Feature Tracking with Event Cameras

Mikihiro Ikura, Cedric Le Gentil, Marcus G. Müller, Florian Schuler, Atsushi Yamashita, Wolfgang Stürzl
IROS 2024poster

Vision-based self-localization is a crucial technology for enabling autonomous robot navigation in GPS-deprived environments. However, standard frame cameras are subject to motion blur and suffer from a limited dynamic range. This research focuses on efficient feature tracking for self-localization…

Cited by 1Source ↗code ↗Scholar ↗
2024

Real-Time Truly-Coupled Lidar-Inertial Motion Correction and Spatiotemporal Dynamic Object Detection

Cedric Le Gentil, Raphael Falque, Teresa Vidal-Calleja
IROS 2024poster

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 surroundings in the form of 3D scans. Unfortunately, most of nowadays lidars do not take snapshots of the environment but s…

Cited by 4Source ↗Scholar ↗
2023

Continuous-Time Gaussian Process Motion-Compensation for Event-Vision Pattern Tracking with Distance Fields

Cedric Le Gentil, Ignacio Alzugaray, Teresa Vidal-Calleja
ICRA 2023poster

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 each of the pixels upon changes in the observed intensity. Providing great advantages in low-light and rapid-motion scen…

Cited by 6Source ↗Scholar ↗
2023

Global Localisation in Continuous Magnetic Vector Fields Using Gaussian Processes

William McDonald, Cedric Le Gentil, Teresa A. Vidal-Calleja
ICASSP 2023accepted

Localisation is one of the key capabilities for autonomous robots with sensors. Magnetic sensors to perceive the environment, although less explored, are an alternative modality to aid localisation. This paper proposes the use of continuous vector fields provided by a Gaussian Process (GP) with a di…

Cited by 0Source ↗Scholar ↗
2023

Pseudo Inputs Optimisation for Efficient Gaussian Process Distance Fields

Lan Wu, Cedric Le Gentil, Teresa Vidal-Calleja
IROS 2023poster

Robots reason about the environment through dedicated representations. Despite the fact that Gaussian Process (GP)-based representations are appealing due to their probabilistic and continuous nature, the cubic computational complexity is a concern. In this paper, we present a novel efficient GP-bas…

Cited by 5Source ↗Scholar ↗
2022

A Tightly-Coupled Event-Inertial Odometry using Exponential Decay and Linear Preintegrated Measurements

Benny Dai, Cedric Le Gentil, Teresa Vidal-Calleja
IROS 2022poster

In this paper, we introduce an event-based visual odometry and mapping framework that relies on decaying event-based corners. Event cameras, unlike conventional cam-eras, can provide sensor data during high-speed motions or in scenes with high dynamic ranges. Rather than providing intensity informat…

Cited by 12Source ↗Scholar ↗
2020

Gaussian Process Gradient Maps for Loop-Closure Detection in Unstructured Planetary Environments

Cedric Le Gentil, Mallikarjuna Vayugundla, Riccardo Giubilato, Wolfgang Stürzl, Teresa Vidal-Calleja, Rudolph Triebel
IROS 2020poster

The ability to recognize previously mapped locations is an essential feature for autonomous systems. Unstructured planetary-like environments pose a major challenge to these systems due to the similarity of the terrain. As a result, the ambiguity of the visual appearance makes state-of-the-art visua…

Cited by 18Source ↗Scholar ↗
2020

IDOL: A Framework for IMU-DVS Odometry using Lines

Cedric Le Gentil, Florian Tschopp, Ignacio Alzugaray, Teresa Vidal-Calleja, Roland Siegwart, Juan Nieto
IROS 2020poster

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 asynchronous streams of events triggered upon illumination changes for each individual pixel. This novel paradigm presents adv…

Cited by 52Source ↗Scholar ↗
2020

Information Driven Self-Calibration for Lidar-Inertial Systems

Mitchell Usayiwevu, Cedric Le Gentil, Jasprabhjit Mehami, Chanyeol Yoo, Robert Fitch, Teresa Vidal-Calleja
IROS 2020poster

Multi-modal estimation systems have the advantage of increased accuracy and robustness. To achieve accurate sensor fusion with these types of systems, a reliable extrinsic calibration between each sensor pair is critical. This paper presents a novel self-calibration framework for lidar-inertial syst…

Cited by 7Source ↗Scholar ↗
2018

3D Lidar-IMU Calibration Based on Upsampled Preintegrated Measurements for Motion Distortion Correction

Cedric Le Gentil, Teresa Vidal-Calleja, Shoudong Huang
ICRA 2018poster

In this paper, we present a probabilistic framework to recover the extrinsic calibration parameters of a lidar-IMU sensing system. Unlike global-shutter cameras, lidars do not take single snapshots of the environment. Instead, lidars collect a succession of 3D-points generally grouped in scans. If t…

Cited by 108Source ↗Scholar ↗