Multi-Session Visual SLAM for Illumination Invariant Re-Localization in Indoor Environments
This addresses re-localization issues for robots in indoor environments with varying illumination, though it is incremental as it builds on existing visual SLAM methods.
The paper tackles illumination changes causing re-localization failures in visual SLAM for robots by creating a multi-session map with variations under different lighting conditions, resulting in improved re-localization capability at any time of day, as demonstrated with various visual features in real-world tests.
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.