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Multi-Session Visual SLAM for Illumination-Invariant Re-Localization in Indoor Environments

Description: For robots navigating using only a camera, illumination changes in indoor environments can cause re-localization failures during autonomous navigation. In this paper, a multi-session visual SLAM approach is presented to create a map made of multiple variations of the same locations in different illumination conditions. The multi-session map can then be used at any hour of the day for improved re-localization capability. The approach presented is independent of the visual features used, and this is demonstrated by comparing re-localization performance between multi-session maps created using the RTAB-Map library with SURF, SIFT, BRIEF, BRISK, KAZE, DAISY and SuperPoint visual features. The approach is tested on six mapping and six localization sessions recorded at 30 minute intervals during sunset using a Google Tango phone in a real apartment, which is the dataset provided here.

This dataset has been used to evaluate the approach used in this paper:

M. Labbé and F. Michaud, “Multi-Session Visual SLAM for Illumination-Invariant Re-Localization in Indoor Environments,” in Frontiers in Robotics and AI, vol. 9, 2022.
Notes: IntRoLab:
Authors: Labbé, Mathieu; Université de Sherbrooke; ORCID iD 0000-0003-0778-5595
Keywords: Localization
Visual SLAM
Feature Matching
Mobile Robotics
Field of Research: 
Electrical engineering, computer engineering, and information engineering
Computer engineering
Computer engineering, not elsewhere classified
Publication Date: 2024-04-05
Publisher: Federated Research Data Repository / dépôt fédéré de données de recherche
Funder: Natural Sciences and Engineering Research Council; RGPIN-2016-05096
Fonds de Recherche du Québec – Nature et Technologies
INTER Strategic Network; 2020-RS4-265381, 2018-RS-203302
Related Identifiers: 

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Access to this dataset is subject to the following terms:
Creative Commons Public Domain Dedication (CC0 1.0)
Labbé, M. (2024). Multi-Session Visual SLAM for Illumination-Invariant Re-Localization in Indoor Environments. Federated Research Data Repository.