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.

翻译：在火灾环境中安全移动是部署于灾难响应的自主移动机器人的关键能力。本文提出一种新颖方法，使移动机器人能够通过构建实时热辐射场来理解火灾。我们通过配准深度图像与热成像图像，获得带有温度标注的三维点云数据。基于这些数据，我们识别火源并利用斯特藩-玻尔兹曼定律估算空白区域的热辐射强度，从而构建覆盖整个环境的连续热辐射场。研究表明，该表征可用于机器人导航：我们将热约束嵌入代价地图以计算无碰撞且热安全的路径。我们在受控实验环境中使用波士顿动力Spot机器人验证了该方法。实验证明机器人能够在规避危险区域的同时抵达导航目标。本方法为移动机器人自主部署于火灾环境开辟了新途径，在搜救、消防及危险物质响应等领域具有潜在应用价值。