@inproceedings{9f2c810ca4a44901b80c75ca3f4c0709,
title = "Real-Time Autonomous Localization in Multi-Robot Scene",
abstract = "In the multi-robot scene, the rapidly changing environment makes it hard for robots to localize. ICP (Iterative Closest Point) is a robust point cloud matching algorithm that can be used for real-time autonomous localization. However, the occlusion of lidar by other robots or humans leads to the failure of traditional ICP localization methods. We propose a novel real-time autonomous localization method combining the improved ICP method with the lidar-odometry mixed localization mode. This method can realize high-precision and fast real-time autonomous localization in complex environments. We provide experimental results and comparisons to other well-known methods to show the competitiveness of the proposed method.",
keywords = "ICP, Lidar-odometry, Multi-robot, Real-time autonomous localization",
author = "Yifan Mao and Zhihao Sun and Xuan Zhou and Fang Deng",
note = "Publisher Copyright: {\textcopyright} 2023 Technical Committee on Control Theory, Chinese Association of Automation.; 42nd Chinese Control Conference, CCC 2023 ; Conference date: 24-07-2023 Through 26-07-2023",
year = "2023",
doi = "10.23919/CCC58697.2023.10240683",
language = "English",
series = "Chinese Control Conference, CCC",
publisher = "IEEE Computer Society",
pages = "4207--4212",
booktitle = "2023 42nd Chinese Control Conference, CCC 2023",
address = "United States",
}