Degradation-Aware LiDAR-Thermal-Inertial SLAM

Abstract

During robotic disaster relief missions, state estimation still faces significant challenges, especially when GNSS is denied or sensor perception undergoes degradation. In this paper, we introduce a degradation-aware LiDAR-Thermal-Inertial SLAM, DaLiTI, that leverages the complementary nature of multi-modal information to achieve robust and precise state estimation in perceptually challenging environments. The system utilizes an iterated error state Kalman filter (IESKF) to loosely integrate LiDAR, thermal infrared camera, and IMU measurements. We propose an adaptive fusion mechanism that dynamically weights and fuses LiDAR and thermal measurements based on real-time modal quality to prevent failure information from propagating throughout the system. Experimental results demonstrate that, compared with state-of-the-art methods, DaLiTI maintains competitive performance in conventional environments and exhibits superior robustness and accuracy in degraded scenarios such as fire scenes or chemical plants with gas leaks. Our implementation is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/HITSZ-NRSL/DaLiTI</uri>.