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
this https URL

本文提出了一种新的地图稀疏化方法，采用二维离散化网格来选择均匀分布的地标，同时基于三维离散化网格引入一个空间约束条件来减少不同空间分布对定位性能的影响，实验表明该方法在效率和定位性能方面都有优越性。

基于 2D 和 3D 离散化网格的高效地图稀疏化

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

We address the problem of map sparsification for long-term visual
localization. For map sparsification, a commonly employed assumption is that
the pre-build map and the later captured localization query are consistent.
However, this assumption can be easily violated in the dynamic world.
Additionally, the map size grows as new data accumulate through time, causing
large data overhead in the long term. In this paper, we aim to overcome the
environmental changes and reduce the map size at the same time by selecting
points that are valuable to future localization. Inspired by the recent
progress in Graph Neural Network(GNN), we propose the first work that models
SfM maps as heterogeneous graphs and predicts 3D point importance scores with a
GNN, which enables us to directly exploit the rich information in the SfM map
graph. Two novel supervisions are proposed: 1) a data-fitting term for
selecting valuable points to future localization based on training queries; 2)
a K-Cover term for selecting sparse points with full map coverage. The
experiments show that our method selected map points on stable and widely
visible structures and outperformed baselines in localization performance.

本文介绍了一种使用图神经网络模型建模三维稠密地图为异构图并预测三维点重要性得分的方法，以选取具有未来重定位价值和稀疏地图覆盖的点。该方法在自适应环境中提取出稳定和广泛可见的地图点，并在定位性能方面优于基线模型。