Localization is a key challenge in many robotics applications. In this work we explore LIDAR-based global localization in both urban and natural environments and develop a method suitable for online application. Our approach leverages efficient deep learning architecture capable of learning compact point cloud descriptors directly from 3D data. The method uses an efficient feature space representation of a set of segmented point clouds to match between the current scene and the prior map. We show that down-sampling in the inner layers of the network can significantly reduce computation time without sacrificing performance. We present substantial evaluation of LIDAR-based global localization methods on nine scenarios from six datasets varying between urban, park, forest, and industrial environments. Part of which includes post-processed data from 30 sequences of the Oxford RobotCar dataset, which we make publicly available. Our experiments demonstrate a factor of three reduction of computation, 70% lower memory consumption with marginal loss in localization frequency. The proposed method allows the full pipeline to run on robots with limited computation payload such as drones, quadrupeds, and UGVs as it does not require a GPU at run time.

翻译：定位是许多机器人应用中的关键挑战。本研究探索了基于LiDAR的全局定位方法在城市与自然环境中的应用，并开发了一种适用于在线运行的方案。该方法采用高效的深度学习架构，能够直接从三维数据中学习紧凑的点云描述符。通过利用分割点云集合的高效特征空间表示，该方法实现了当前场景与先验地图的匹配。研究证明，对网络内部层进行下采样可在不牺牲性能的前提下显著降低计算时间。我们在六个数据集的九种场景（涵盖城市、公园、森林及工业环境）中对基于LiDAR的全局定位方法进行了全面评估，其中包含经后处理的牛津RobotCar数据集30个序列的数据（已公开）。实验表明，该方法在定位频率仅出现轻微损失的情况下，实现了计算量降低三分之二、内存消耗减少70%。由于运行时无需GPU，所提方法可使完整处理流水线在计算负载受限的机器人（如无人机、四足机器人和无人地面车辆）上运行。