A Loosely Coupled Vision-LiDAR Odometry using Covariance Intersection Filtering - Archive ouverte HAL Accéder directement au contenu
Communication Dans Un Congrès Année : 2021

A Loosely Coupled Vision-LiDAR Odometry using Covariance Intersection Filtering

Résumé

This paper presents a loosely-coupled sensor fusion approach, which efficiently combines complementary visual and range sensor information to estimate the vehicle ego-motion. Descriptor-based and distance-based matching strategies are respectively applied to visual and range measurements for feature tracking. Nonlinear optimization optimally estimates the relative pose across consecutive frames and an uncertainty analysis using forward and backward covariance propagation is made to model the estimation accuracy. Covariance intersection filter paves the way for us to loosely couple stereo vision and LiDAR odometry considering respective uncertainties. We evaluate our approach with KITTI dataset which shows its effectiveness to fierce rotational motion and temporary absence of visual features, achieving the average relative translation error of 0.84% for the challenging 01 sequence on the highway.
Fichier principal
Vignette du fichier
final_version_IV2021_Songming_Chen.pdf (302.13 Ko) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)

Dates et versions

hal-03413629 , version 1 (09-02-2022)

Identifiants

Citer

Songming Chen, Vincent Frémont. A Loosely Coupled Vision-LiDAR Odometry using Covariance Intersection Filtering. 2021 IEEE Intelligent Vehicles Symposium (IV), Jul 2021, Nagoya, Japan. pp.1102-1107, ⟨10.1109/IV48863.2021.9575275⟩. ⟨hal-03413629⟩
73 Consultations
131 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More