Accurate localization is an essential technology for the flexible navigation of robots in large-scale environments. Both SLAM-based and map-based localization will increase the computing load due to the increase in map size, which will affect downstream tasks such as robot navigation and services. To this end, we propose a localization system based on Block Maps (BMs) to reduce the computational load caused by maintaining large-scale maps. Firstly, we introduce a method for generating block maps and the corresponding switching strategies, ensuring that the robot can estimate the state in large-scale environments by loading local map information. Secondly, global localization according to Branch-and-Bound Search (BBS) in the 3D map is introduced to provide the initial pose. Finally, a graph-based optimization method is adopted with a dynamic sliding window that determines what factors are being marginalized whether a robot is exposed to a BM or switching to another one, which maintains the accuracy and efficiency of pose tracking. Comparison experiments are performed on publicly available large-scale datasets. Results show that the proposed method can track the robot pose even though the map scale reaches more than 6 kilometers, while efficient and accurate localization is still guaranteed on NCLT and M2DGR.

翻译：精确的定位是实现机器人在大规模环境中灵活导航的关键技术。无论是基于SLAM还是地图的定位方法，地图规模的增大会导致计算负荷上升，进而影响机器人导航、服务等下游任务。针对这一问题，本文提出了一种基于分块地图（Block Maps, BMs）的定位系统，旨在降低维护大规模地图带来的计算负担。首先，我们介绍了分块地图的生成方法及相应的切换策略，确保机器人通过加载局部地图信息即可在大规模环境中完成状态估计。其次，引入了基于三维地图中分支定界搜索（Branch-and-Bound Search, BBS）的全局定位方法，用于提供初始位姿。最后，采用基于图的优化方法，并结合动态滑动窗口机制，根据机器人处于同一分块地图或切换至另一分块地图的情况，确定需边缘化的因子，从而保持位姿跟踪的精度与效率。在公开的大规模数据集上进行了对比实验。结果表明，即使地图规模超过6公里，该方法仍能有效跟踪机器人位姿，且在NCLT和M2DGR数据集上保持了高效且精确的定位性能。