Multi-Session Visual SLAM for Illumination Invariant Re-Localization in Indoor Environments
Not on the map. Why?
Abstract (arXiv)
For robots navigating using only a camera, illumination changes in indoor environments can cause re-localization failures during autonomous navigation. In this paper, we present a multi-session visual SLAM approach 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.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 9 of its 57 references that are in the map. First posted on arXiv 5 Mar 2021.
Builds on
9 of 57 references.
References · 9
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Built on by
Citing papers per year: 2023: 2; 2024: 1; 2025: 1.
4 papers.
- 2024Multi-Session SLAM with Differentiable Wide-Baseline Pose Optimizationcited by 6
- 2023Visual Semantic Navigation with Real Robotscited by 2
- 2023CyberLoc: Towards Accurate Long-term Visual Localizationcited by 0
- 2025LT-Exosense: A Vision-centric Multi-session Mapping System for Lifelong Safe Navigation of Exoskeletonscited by 0
- 2025LT-Exosense: A Vision-centric Multi-session Mapping System for Lifelong Safe Navigation of Exoskeletons25 Oct 2025
- 2024Multi-Session SLAM with Differentiable Wide-Baseline Pose Optimization23 Apr 2024
- 2023Visual Semantic Navigation with Real Robots28 Nov 2023
- 2023CyberLoc: Towards Accurate Long-term Visual Localization6 Jan 2023