融合热成像与可见光，实时增强3D地图语义信息
Multimodal Signal Processing For Thermo-Visible-Lidar Fusion In Real-time 3D Semantic Mapping
- 像素级融合可见光与红外图像，投影点云到融合图
- 实时识别高温目标，将其作为语义层注入3D地图
- 适合灾害评估、工业巡检等需要温度感知的场景
在复杂环境中，自主机器人导航与环境感知对SLAM技术提出更高要求。本文提出一种新方法，通过将热成像信息融入3D点云地图以实现语义增强。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中。随后在热通道中分割热源特征，即时识别高温目标，并将温度信息作为语义层叠加至最终3D地图。该方法生成的地图不仅几何精确，还具备关键环境语义理解能力，在快速灾情评估和工业预防性维护等特定应用中具有重要价值。
原文摘要 · Abstract (English)
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.
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