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.

翻译：本文提出了一种高效混合定位框架，用于无人地面车辆在崎岖或不平坦地形中的自主导航，以及三维点云数据的精细处理技术。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。系统基于混合方案，不仅使机器人能够在预建地图中进行定位，还能同时执行即时定位与地图构建以探索未知场景，并构建与现有地图对齐的扩展地图。我们的框架专为位于崎岖地形中的高压变电站自主地面巡检任务而开发。通过使用变电站预建地图的实地试验，我们展示了算法的应用，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兴趣点，并利用视觉和热成像数据执行巡检任务。