LST-SLAM: A Stereo Thermal SLAM System for Kilometer-Scale Dynamic Environments
This addresses the challenge of reliable robot perception in adverse conditions for robotics applications, though it is an incremental advancement in thermal SLAM.
The paper tackles the problem of robust thermal SLAM in large-scale dynamic outdoor environments by proposing LST-SLAM, which combines self-supervised feature learning, stereo motion tracking, and hybrid constraints, achieving significant improvements in robustness and accuracy over existing systems like AirSLAM and DROID-SLAM on kilometer-scale datasets.
Thermal cameras offer strong potential for robot perception under challenging illumination and weather conditions. However, thermal Simultaneous Localization and Mapping (SLAM) remains difficult due to unreliable feature extraction, unstable motion tracking, and inconsistent global pose and map construction, particularly in dynamic large-scale outdoor environments. To address these challenges, we propose LST-SLAM, a novel large-scale stereo thermal SLAM system that achieves robust performance in complex, dynamic scenes. Our approach combines self-supervised thermal feature learning, stereo dual-level motion tracking, and geometric pose optimization. We also introduce a semantic-geometric hybrid constraint that suppresses potentially dynamic features lacking strong inter-frame geometric consistency. Furthermore, we develop an online incremental bag-of-words model for loop closure detection, coupled with global pose optimization to mitigate accumulated drift. Extensive experiments on kilometer-scale dynamic thermal datasets show that LST-SLAM significantly outperforms recent representative SLAM systems, including AirSLAM and DROID-SLAM, in both robustness and accuracy.