Accurate Localization Algorithm for Indoor Robots Using Branch and Bound Strategy

被引:1
|
作者
Wang, Yuyang [1 ]
Cui, Zipeng [1 ]
Zhang, Lei [2 ]
Luo, Xiaochuan [1 ]
Sun, Jie [3 ]
机构
[1] Northeastern Univ, Coll Informat Sci & Engn, Shenyang, Liaoning, Peoples R China
[2] EuroDAO SAS, St Quentin en Yvelines, France
[3] Northeastern Univ, State Key Lab Rolling & Automat, Shenyang, Liaoning, Peoples R China
来源
PROCEEDINGS OF 2022 IEEE INTERNATIONAL CONFERENCE ON MECHATRONICS AND AUTOMATION (IEEE ICMA 2022) | 2022年
基金
国家重点研发计划; 中国国家自然科学基金;
关键词
Global Localization; Position Tracking; Branch and Bound;
D O I
10.1109/ICMA54519.2022.9856252
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Accurate localization is a prerequisite for robots to realize autonomous navigation. This paper proposes a robot localization algorithm suitable for indoor environments to effectively solve the problem of global localization and position tracking. First, global localization is realized by employing a two-stage matching method involving coarse and fine levels based on a branch-and-bound algorithm and iterative nearest point (ICP) algorithm to estimate the initial pose of the robot. Second, a local map-based scan matching method is used to realize position tracking. To address the accumulated errors between local maps, we build a pose graph to achieve loop-closure optimization. Moreover, to decrease the computational complexity of loopclosure detection, the branch-and-bound algorithm is used to accelerate the search of the loop constraints. The performance of the proposed algorithm is comprehensively evaluated in real application scenarios using commercial logistics robots. The experimental results demonstrate that represents a highly accurate and robust localization algorithm.
引用
收藏
页码:98 / 103
页数:6
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