基于热成像的千米级动态环境定位建图系统，提升复杂场景下稳定性与精度。
LST-SLAM: A Stereo Thermal SLAM System for Kilometer-Scale Dynamic Environments
- 自监督特征学习+双层运动跟踪，增强热成像下的定位鲁棒性。
- 在千米级动态环境中，定位误差显著低于AirSLAM和DROID-SLAM。
- 适合户外复杂光照或恶劣天气下的机器人自主导航任务。
热成像相机在恶劣光照与天气条件下具备强大的机器人感知潜力。然而，在动态大尺度室外环境中，热成像同时定位与地图构建（SLAM）仍面临特征提取不可靠、运动追踪不稳定及全局位姿与地图不一致等挑战。为此，本文提出LST-SLAM，一种新型大规模立体热成像SLAM系统，可在复杂动态场景中实现稳健性能。方法结合自监督热特征学习、立体双层运动追踪与几何位姿优化，并引入语义-几何混合约束，抑制缺乏强帧间几何一致性的潜在动态特征。此外，设计在线增量词袋模型用于回环检测，配合全局位姿优化以缓解累积漂移。在千米级动态热成像数据集上的大量实验表明，LST-SLAM在鲁棒性与精度上均显著优于近期代表性SLAM系统，包括AirSLAM和DROID-SLAM。
原文摘要 · Abstract (English)
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.
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