Evaluation of a novel DSO-based Indoor Ceiling-Vision Odometry System - Systèmes et Applications des Technologies de l'Information et de l'Energie Accéder directement au contenu
Communication Dans Un Congrès Année : 2022

Evaluation of a novel DSO-based Indoor Ceiling-Vision Odometry System

Résumé

Indoor localization for mobile industrial robots is a crucial step toward an autonomous system. A mobile robot needs a reliable and robust localization system to achieve its task autonomously. A reasonable estimate of the robot’s state can be achieved through Visual Odometry (VO); however, with dynamic objects in the scene, classical VO approaches need to detect and filter these moving objects. Alternatively, we can use an up-facing camera to track the movement with respect to the ceiling, which represents a static and invariant space. This paper presents Ceiling-DSO, an indoor ceiling-vision (CV) system based on Direct Sparse Odometry (DSO). We take advantage of the generic formulation of DSO to avoid making assumptions about the observable shapes or landmarks on the ceiling, making the method generic and applicable to multiple ceiling types. We built a ceiling-vision dataset in a real-world scenario; we then used it to test our approach with different DSO parameters to identify the best fit for robot pose estimation. This paper provides a qualitative and quantitative analysis of the obtained results that showed an acceptable error rate compared to the ground truth.
Fichier principal
Vignette du fichier
icarcv-2022-ceiling-dso.pdf (2.46 Mo) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)

Dates et versions

hal-03935953 , version 1 (12-01-2023)

Identifiants

Citer

Abdelhak Bougouffa, Emmanuel Seignez, Samir Bouaziz, Florian Gardes. Evaluation of a novel DSO-based Indoor Ceiling-Vision Odometry System. 2022 17th International Conference on Control, Automation, Robotics and Vision (ICARCV 2022)), Nanyang Technological University, Singapore (School of Electrical and Electronic Engineering), Dec 2022, Singapour, Singapore. pp.47-53, ⟨10.1109/ICARCV57592.2022.10004272⟩. ⟨hal-03935953⟩
45 Consultations
63 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More