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

TL;DR

This paper introduces a multi-session visual SLAM method that creates illumination-invariant maps for indoor robot re-localization, significantly improving robustness across different lighting conditions.

Contribution

The proposed approach is independent of visual features and effectively enhances re-localization in varying illumination by combining multiple session maps.

Findings

Multi-session maps improve re-localization accuracy across different lighting conditions.

The method is tested successfully with various visual features including SURF, SIFT, and SuperPoint.

Effective in real indoor environments with natural illumination changes.

Abstract

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.