用热辐射场让机器人实时感知火灾，安全避障导航。
Understanding Fire Through Thermal Radiation Fields for Mobile Robots
- 融合深度与热成像构建带温标的3D点云，生成连续热辐射场。
- 基于斯特藩-玻尔兹曼定律估算空域热辐射，实现热环境建模。
- 将热约束嵌入代价图，指导机器人避险前行，适合灾害救援场景。
自主移动机器人在灾难响应中穿越火灾环境的能力至关重要。本文提出一种新方法，通过构建实时热辐射场来理解火灾。我们将深度图像与热成像配准，生成带有温度值的3D点云，识别火源后利用斯特藩-玻尔兹曼定律估算空域热辐射，从而构建连续的环境热辐射场。该表示可用于机器人导航，通过将热约束嵌入代价地图，计算出无碰撞且热安全的路径。我们在波士顿动力Spot机器人上进行了受控实验验证，结果表明机器人能够在避开危险区域的同时成功抵达目标。该方法为在火灾环境中自主部署机器人提供了可能，适用于搜救、灭火及危化品处置等场景。
原文摘要 · Abstract (English)
Safely moving through environments affected by fire is a critical capability for autonomous mobile robots deployed in disaster response. In this work, we present a novel approach for mobile robots to understand fire through building real-time thermal radiation fields. We register depth and thermal images to obtain a 3D point cloud annotated with temperature values. From these data, we identify fires and use the Stefan-Boltzmann law to approximate the thermal radiation in empty spaces. This enables the construction of a continuous thermal radiation field over the environment. We show that this representation can be used for robot navigation, where we embed thermal constraints into the cost map to compute collision-free and thermally safe paths. We validate our approach on a Boston Dynamics Spot robot in controlled experimental settings. Our experiments demonstrate the robot's ability to avoid hazardous regions while still reaching navigation goals. Our approach paves the way toward mobile robots that can be autonomously deployed in fire-affected environments, with potential applications in search-and-rescue, firefighting, and hazardous material response.
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