  For robots navigating using only a camera, illumination changes in indoor
environments can cause 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 localization capability. The approach presented is independent of the
visual features used, and this is demonstrated by comparing localization
performance between multi-session maps created using the RTAB-Map library with
SURF, SIFT, BRIEF, FREAK, BRISK, KAZE, DAISY and SuperPoint visual features.
The approach is tested on six mapping and six localization sessions recorded at
30 minutes intervals during sunset using a Google Tango phone in a real
apartment.
