In complex environments, autonomous robot navigation and environmental perception pose higher requirements for SLAM technology. This paper presents a novel method for semantically enhancing 3D point cloud maps with thermal information. By first performing pixel-level fusion of visible and infrared images, the system projects real-time LiDAR point clouds onto this fused image stream. It then segments heat source features in the thermal channel to instantly identify high temperature targets and applies this temperature information as a semantic layer on the final 3D map. This approach generates maps that not only have accurate geometry but also possess a critical semantic understanding of the environment, making it highly valuable for specific applications like rapid disaster assessment and industrial preventive maintenance.

翻译：在复杂环境中，自主机器人导航与环境感知对SLAM技术提出了更高要求。本文提出一种利用热信息增强三维点云语义地图的新方法。系统首先对可见光与红外图像进行像素级融合，再将实时LiDAR点云投影至该融合图像流。通过分割热成像通道中的热源特征，系统可即时识别高温目标，并将温度信息作为语义层叠加至最终三维地图。该方法生成的地图不仅具有精确的几何结构，还能实现对环境的关键语义理解，在快速灾害评估与工业预防性维护等特定应用场景中具有重要价值。