Block-Map-Based Localization in Large-Scale Environment
arxiv(2024)
摘要
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.
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