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 Specimen Collection - Specimen Collection Devices 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, Hockey Accessories - Bags - Carry SIFT, BRIEF, BRISK, KAZE, DAISY, and SuperPoint visual features.
The approach is tested on six mapping and six localization sessions recorded at 30 min intervals during sunset using a Google Tango phone in a real apartment.