Block-Map-Based Localization in Large-Scale Environment

TL;DR

This paper introduces a block map-based localization system that efficiently estimates robot pose in large-scale environments, reducing computational load while maintaining high accuracy over maps exceeding 6 kilometers.

Contribution

The paper presents a novel block map generation and switching strategy, combined with a global localization method and graph-based optimization, to improve large-scale robot localization efficiency and accuracy.

Findings

Successfully tracks robot pose over 6 km maps

Maintains high localization accuracy and efficiency

Demonstrates effectiveness on large-scale datasets

Abstract

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.