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

arXiv: https://arxiv.org/abs/2103.03827 · HTML: https://mlsanity.com/paper/2103.03827/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 57 references that are in the map.
- First posted on arXiv 5 Mar 2021.

## Builds on (most cited first, 9 of 9)

- [SuperGlue: Learning Feature Matching with Graph Neural Networks](https://mlsanity.com/paper/1911.11763/) (2019)
- [SuperPoint: Self-Supervised Interest Point Detection and Description](https://mlsanity.com/paper/1712.07629/) (2017)
- [NetVLAD: CNN architecture for weakly supervised place recognition](https://mlsanity.com/paper/1511.07247/) (2015)
- [From Coarse to Fine: Robust Hierarchical Localization at Large Scale](https://mlsanity.com/paper/1812.03506/) (2018)
- [Visual Camera Re-Localization from RGB and RGB-D Images Using DSAC](https://mlsanity.com/paper/2002.12324/) (2020)
- [On the Performance of ConvNet Features for Place Recognition](https://mlsanity.com/paper/1501.04158/) (2015)
- [Are We Ready for Service Robots? The OpenLORIS-Scene Datasets for Lifelong SLAM](https://mlsanity.com/paper/1911.05603/) (2019)
- [Appearance-Based Landmark Selection for Efficient Long-Term Visual Localization](https://mlsanity.com/paper/1808.02656/) (2018)
- [HDRFusion: HDR SLAM using a low-cost auto-exposure RGB-D sensor](https://mlsanity.com/paper/1604.00895/) (2016)

## Built on by (most cited first, 4 of 4)

- [Multi-Session SLAM with Differentiable Wide-Baseline Pose Optimization](https://mlsanity.com/paper/2404.15263/) (2024)
- [Visual Semantic Navigation with Real Robots](https://mlsanity.com/paper/2311.16623/) (2023)
- [CyberLoc: Towards Accurate Long-term Visual Localization](https://mlsanity.com/paper/2301.02403/) (2023)
- [LT-Exosense: A Vision-centric Multi-session Mapping System for Lifelong Safe Navigation of Exoskeletons](https://mlsanity.com/paper/2510.22164/) (2025)

## 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.