Towards Long Term SLAM on Thermal Imagery

Visual SLAM with thermal imagery remains a difficult problem for many state of the art (SOTA) algorithms. Compared with visible spectrum imagery, thermal imagery generally has lower contrast, higher noise, and tends to have lower resolution, making for challenging front-end data association. Thermal imagery also presents a difficult problem for long term relocalization and map reuse, because the relative temperatures of objects in thermal imagery tend to 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