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Localization in a pre-built map is a basic technique for robot autonomousnavigation. Existing mapping and localization methods commonly work well insmall-scale environments. As a map grows larger, however, more memory isrequired and localization becomes inefficient. To solve these problems, mapsparsification becomes a practical necessity to acquire a subset of theoriginal map for localization. Previous map sparsification methods add aquadratic term in mixed-integer programming to enforce a uniform distributionof selected landmarks, which requires high memory capacity and heavycomputation. In this paper, we formulate map sparsification in an efficientlinear form and select uniformly distributed landmarks based on 2D discretizedgrids. Furthermore, to reduce the influence of different spatial distributionsbetween the mapping and query sequences, which is not considered in previousmethods, we also introduce a space constraint term based on 3D discretizedgrids. The exhaustive experiments in different datasets demonstrate thesuperiority of the proposed methods in both efficiency and localizationperformance. The relevant codes will be released athttps://github.com/fishmarch/SLAM_Map_Compression.

Efficient Map Sparsification Based on 2D and 3D Discretized Grids