Full Text:   <2136>

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CLC number: TP399

On-line Access: 2022-02-28

Received: 2020-07-18

Revision Accepted: 2022-04-22

Crosschecked: 2020-11-18

Cited: 0

Clicked: 3635

Citations:  Bibtex RefMan EndNote GB/T7714

 ORCID:

Wei WEI

https://orcid.org/0000-0002-8998-045X

Xiaorui ZHU

https://orcid.org/0000-0003-1400-059X

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Frontiers of Information Technology & Electronic Engineering  2022 Vol.23 No.2 P.234-245

http://doi.org/10.1631/FITEE.2000358


Novel robust simultaneous localization and mapping for long-term autonomous robots


Author(s):  Wei WEI, Xiaorui ZHU, Yi WANG

Affiliation(s):  School of Mechanical Engineering and Automation, Harbin Institute of Technology, Shenzhen 518055, China; more

Corresponding email(s):   weirui9003@gmail.com, xiaoruizhu@hit.edu.cn, wangyi601@aliyun.com

Key Words:  Simultaneous localization and mapping (SLAM), Long-term, Robustness, Light detection and ranging (LiDaR), Visual inertial LiDaR navigation (VILN)


Wei WEI, Xiaorui ZHU, Yi WANG. Novel robust simultaneous localization and mapping for long-term autonomous robots[J]. Frontiers of Information Technology & Electronic Engineering, 2022, 23(2): 234-245.

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author="Wei WEI, Xiaorui ZHU, Yi WANG",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="23",
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pages="234-245",
year="2022",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.2000358"
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T1 - Novel robust simultaneous localization and mapping for long-term autonomous robots
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Abstract: 
A fundamental task for mobile robots is simultaneous localization and mapping (SLAM). Moreover, long-term robustness is an important property for SLAM. When vehicles or robots steer fast or steer in certain scenarios, such as low-texture environments, long corridors, tunnels, or other duplicated structural environments, most SLAM systems might fail. In this paper, we propose a novel robust visual inertial light detection and ranging (LiDaR) navigation (VILN) SLAM system, including stereo visual-inertial LiDaR odometry and visual-LiDaR loop closure. The proposed VILN SLAM system can perform well with low drift after long-term experiments, even when the LiDaR or visual measurements are degraded occasionally in complex scenes. Extensive experimental results show that the robustness has been greatly improved in various scenarios compared to state-of-the-art SLAM systems.

用于长期自主机器人的新型鲁棒同时定位与建图方法

魏伟1,朱晓蕊1,2,王毅1
1哈尔滨工业大学(深圳)机电工程与自动化学院,中国深圳市,518055
2岭南大数据研究院,中国珠海市,519000
摘要:自主移动机器人的基本任务是同时定位与建图(SLAM)。此外,长期鲁棒性是SLAM的一个重要属性。当车辆或机器人快速旋转或在某些场景中(例如低纹理环境、长走廊、隧道或其他重复的结构环境)转向时,大多数SLAM系统可能会失效。本文提出一种新颖的鲁棒视觉惯性激光雷达(LiDaR)导航(VILN)SLAM系统,包括立体视觉-惯性LiDaR里程计和视觉-LiDaR闭环。所提出的VILN SLAM系统即使在偶尔会降低LiDaR或视觉测量性能的复杂场景中也可以长期稳定地运行。大量实验结果表明,与最先进的SLAM系统相比,VILN SLAM系统在各种场景下的鲁棒性都有了很大提高。

关键词:同时定位与建图(SLAM);长期;鲁棒性;激光雷达(LiDaR);视觉惯性激光雷达导航(VILN)

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

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