Appearance change prediction for long-term navigation across seasons

Changing environments pose a serious problem to current robotic systems aiming at long term operation. While place recognition systems perform reasonably well in static or low-dynamic environments, severe appearance changes that occur between day and night, between different seasons or different local weather conditions remain a challenge. In this paper we propose to learn to predict the changes in an environment. Our key insight is that the occurring appearance changes are in part systematic, repeatable and therefore predictable. The goal of our work is to support existing approaches to place recognition by learning how the visual appearance of an environment changes over time and by using this learned knowledge to predict its appearance under different environmental conditions. We describe the general idea of appearance change prediction (ACP) and a novel implementation based on vocabularies of superpixels (SP-ACP). Despite its simplicity, we can further show that the proposed approach can improve the performance of SeqSLAM and BRIEF-Gist for place recognition on a large-scale dataset that traverses an environment under extremely different conditions in winter and summer.