使用地面车辆在崎岖地形中进行高压变电站的定位与离线建图
Localization and Offline Mapping of High-Voltage Substations in Rough Terrain Using a Ground Vehicle
AI总结：
本文提出一种基于FAST-LIO2的混合定位与建图框架，结合激光雷达和惯性数据，使无人地面车辆能在崎岖地形的高压变电站中实现已知地图定位、未知场景探索与扩展建图，并支持地面可通行区域分析及视觉与热数据巡检。
摘要：
本文提出了一种高效的混合定位框架，用于无人地面车辆在崎岖或不平坦地形中的自主导航，并提出了对3D点云数据进行详细处理的技术。该框架是FAST-LIO2算法的扩展版本，旨在利用激光雷达和惯性数据在已知点云地图中实现鲁棒定位。该系统基于一种混合方案，使机器人不仅能够在预先构建的地图中进行定位，还能同时执行同步定位与建图以探索未知场景，并构建与现有地图对齐的扩展地图。我们的框架是为位于崎岖地形中的高压变电站的自主地面巡检任务而开发的。我们展示了该算法在实地试验中的应用，使用了变电站的预先构建地图，同时分析了旨在分离地面及其可通行区域的技术，以使机器人能够接近地图中的感兴趣点，并利用视觉和热数据执行巡检任务。
英文摘要：
This paper proposes an efficient hybrid localization framework for the autonomous navigation of an unmanned ground vehicle in uneven or rough terrain, as well as techniques for detailed processing of 3D point cloud data. The framework is an extended version of FAST-LIO2 algorithm aiming at robust localization in known point cloud maps using Lidar and inertial data. The system is based on a hybrid scheme which allows the robot to not only localize in a pre-built map, but concurrently perform simultaneous localization and mapping to explore unknown scenes, and build extended maps aligned with the existing map. Our framework has been developed for the task of autonomous ground inspection of high-voltage electrical substations residing in rough terrain. We present the application of our algorithm in field trials, using a pre-built map of the substation, but also analyze techniques that aim to isolate the ground and its traversable regions, to allow the robot to approach points of interest within the map and perform inspection tasks using visual and thermal data.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
