Article Dans Une Revue IEEE Robotics and Automation Letters Année : 2024

SaDVIO : Sparsify and Densify VIO for UGV Traversability Estimation

Résumé

Visual navigation has achieved significant maturity in recent decades. Indirect methods relying on sparse feature extraction, have become a standard for state estimation. However, these techniques are not frequently utilized as inputs for path planning due to the sparse nature of their maps. To address this limitation, this paper introduces a method to densify the sparse map generated by local indirect Visual or Visual Inertial Odometry (VO-VIO) based on factor graph sparsification. The intended application is the autonomous navigation of Unmaned Ground Vehicles (UGV) in planetary environments without the need to perform computationally expensive dense stereo vision. An incremental 3D mesh is built upon a 2D triangulation of the features identified by the VO. A photometric consistency check is conducted on each triangle, resulting in a triangle soup rather than a manifold mesh. A dense point cloud is then extracted by casting rays on the triangle soup and using the most reliable triangle to compute depth. Geometric traversability information can subsequently be derived from this point cloud to aid UGV navigation. This system is evaluated on a dataset recorded on an artificial Martian terrain and demonstrates superior performance compared to a similar method from the state-of-the-art. Both the dataset and source code are released as open source at https://github.com/ISAE-PNX/SaDVIO.

Fichier principal
Vignette du fichier
RAL_2024___SaDVIO.pdf (7.31 Mo) Télécharger le fichier
Origine Fichiers produits par l'(les) auteur(s)
Licence

Dates et versions

hal-04639114 , version 1 (08-07-2024)

Licence

Identifiants

  • HAL Id : hal-04639114 , version 1

Citer

César Debeunne, Alex Torres, Damien Vivet. SaDVIO : Sparsify and Densify VIO for UGV Traversability Estimation. IEEE Robotics and Automation Letters, In press. ⟨hal-04639114⟩
253 Consultations
640 Téléchargements

Partager

  • More