用热成像实现夜间精准定位与建图，解决低光下视觉导航难题。
SLAM in the Dark: Self-Supervised Learning of Pose, Depth and Loop-Closure from Thermal Images
- 融合注意力机制提升热图像位姿与深度估计精度
- 在夜间复杂场景中实现亚米级定位与稠密三维重建
- 适合无人机、机器人夜间自主导航应用
视觉SLAM对移动机器人、无人机导航和虚拟现实至关重要，但传统RGB相机在低光环境下表现不佳，促使热成像SLAM的发展。然而，热成像存在对比度低、噪声高及缺乏大规模标注数据集等问题，限制了深度学习在户外场景的应用。本文提出DarkSLAM，一种基于深度学习的单目热成像SLAM系统，适用于复杂光照条件下的大范围定位与重建。方法上，在视觉里程计中引入高效通道注意力（ECA），在深度估计中采用选择性核注意力（SKA），以提升位姿精度并缓解热图像深度退化。此外，系统还集成基于热深度的回环检测与位姿优化，增强在低纹理热图像场景中的鲁棒性。大量室外实验表明，DarkSLAM显著优于SC-Sfm-Learner和Shin等现有方法，在夜间挑战性环境中仍能实现精确的定位与3D稠密映射。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。