Towards Long Term SLAM on Thermal Imagery
Colin Keil Aniket Gupta Pushyami Kaveti Hanumant Singh
Visual SLAM with thermal imagery, and other low contrast visually degraded
environments such as underwater, or in areas dominated by snow and ice, remain
a difficult problem for many state of the art (SOTA) algorithms. In addition to
challenging front-end data association, thermal imagery presents an additional
difficulty for long term relocalization and map reuse. The relative
temperatures of objects in thermal imagery change dramatically from day to
night. Feature descriptors typically used for relocalization in SLAM are unable
to maintain consistency over these diurnal changes. We show that learned
feature descriptors can be used within existing Bag of Word based localization
schemes to dramatically improve place recognition across large temporal gaps in
thermal imagery. In order to demonstrate the effectiveness of our trained
vocabulary, we have developed a baseline SLAM system, integrating learned
features and matching into a classical SLAM algorithm. Our system demonstrates
good local tracking on challenging thermal imagery, and relocalization that
overcomes dramatic day to night thermal appearance changes. Our code and
datasets are available here:
https://github.com/neufieldrobotics/IRSLAM_Baseline