Block-Map-Based Localization in Large-Scale Environment
<|reference_start|>Block-Map-Based Localization in Large-Scale Environment: 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.<|reference_end|>
@article{feng2024block-map-based,
    title={Block-Map-Based Localization in Large-Scale Environment},
    author={Yixiao Feng, Zhou Jiang, Yongliang Shi, Yunlong Feng, Xiangyu Chen,
  Hao Zhao, Guyue Zhou},
    journal={arXiv preprint arXiv:2404.18192},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.18192},
    primaryClass={cs.RO}
}