SLAM in the Dark: Self-Supervised Learning of Pose, Depth and Loop-Closure from Thermal Images

SLAM in the Dark: Self-Supervised Learning of Pose, Depth and Loop-Closure from Thermal Images Y angfan Xu, Qu Hao, Lilian Zhang, Jun Mao, Xiaofeng He, Wenqi Wu, Changhao Chen* Abstract -- Visual SLAM is essential for mobile robots, drone navigation, and VR/AR, but traditional RGB camera systems struggle in low-light conditions, driving interest in thermal SLAM, which excels in such environments. However, thermal imaging faces challenges like low contrast, high noise, and limited large-scale annotated datasets, restricting the use of deep learning in outdoor scenarios. We present DarkSLAM, a noval deep learning-based monocular thermal SLAM system designed for large-scale localization and reconstruction in complex lighting conditions.Our approach incorporates the Efficient Channel Attention (ECA) mechanism in visual odometry and the Selective Kernel Attention (SKA) mechanism in depth estimation to enhance pose accuracy and mitigate thermal depth degradation. Additionally, the system includes thermal depth-based loop closure detection and pose optimization, ensuring robust performance in low-texture thermal scenes. Extensive outdoor experiments demonstrate that DarkSLAM significantly outperforms existing methods like SC-Sfm-Learner and Shin et al., delivering precise localization and 3D dense mapping even in challenging nighttime environments. I. INTRODUCTION Simultaneous Localization and Mapping (SLAM) is crucial for intelligent systems from mobile robots, drones, to self-driving vehicles, enabling their real-time localization and mapping for autonomous navigation. Traditional visual SLAM systems, which rely on visible-light (RGB) cameras, struggle in challenging lighting conditions such as strong light, shadows, or nighttime, limiting their use in all-time scenarios. Thermal cameras, which detect heat radiation, offer a solution by functioning in darkness, smoke, and dust, complementing visible-light sensors. Recent improvements in thermal camera resolution and sensitivity have increased their reliability in autonomous systems.