Efficient Map Sparsification Based on 2D and 3D Discretized Grids
This work addresses memory and computational bottlenecks for robot navigation in large environments, representing an incremental improvement over prior methods.
The paper tackles the problem of inefficient localization in large-scale robot maps by proposing an efficient map sparsification method based on 2D and 3D discretized grids, resulting in improved efficiency and localization performance as demonstrated in experiments across different datasets.
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.