Localization in a pre-built map is a basic technique for robot autonomous navigation. Existing mapping and localization methods commonly work well in small-scale environments. As a map grows larger, however, more memory is required and localization becomes inefficient. To solve these problems, map sparsification becomes a practical necessity to acquire a subset of the original map for localization. Previous map sparsification methods add a quadratic term in mixed-integer programming to enforce a uniform distribution of selected landmarks, which requires high memory capacity and heavy computation. In this paper, we formulate map sparsification in an efficient linear form and select uniformly distributed landmarks based on 2D discretized grids. Furthermore, to reduce the influence of different spatial distributions between the mapping and query sequences, which is not considered in previous methods, we also introduce a space constraint term based on 3D discretized grids. The exhaustive experiments in different datasets demonstrate the superiority of the proposed methods in both efficiency and localization performance. The relevant codes will be released at https://github.com/fishmarch/SLAM_Map_Compression.

翻译：基于预构建地图的定位是机器人自主导航的基础技术。现有建图与定位方法在小规模环境中通常表现良好，但当地图规模增大时，内存需求增加且定位效率降低。为解决这些问题，地图稀疏化成为从原始地图中获取定位所需子集的实用方法。此前的地图稀疏化方法在混合整数规划中引入二次项以强制实现所选地标的均匀分布，这需要较高的内存容量和计算量。本文提出一种高效线性形式的地图稀疏化方法，基于二维离散网格选择均匀分布的地标。此外，为降低建图序列与查询序列之间空间分布差异的影响（这一问题尚未被现有方法考虑），我们还引入了基于三维离散网格的空间约束项。在不同数据集上的充分实验表明，所提方法在效率与定位性能方面均具有优越性。相关代码将发布于https://github.com/fishmarch/SLAM_Map_Compression。