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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

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Citations:  Bibtex RefMan EndNote GB/T7714




Xiaorui ZHU


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


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.

@article{title="Novel robust simultaneous localization and mapping for long-term autonomous robots",
author="Wei WEI, Xiaorui ZHU, Yi WANG",
journal="Frontiers of Information Technology & Electronic Engineering",
publisher="Zhejiang University Press & Springer",

%0 Journal Article
%T Novel robust simultaneous localization and mapping for long-term autonomous robots
%A Wei WEI
%A Xiaorui ZHU
%J Frontiers of Information Technology & Electronic Engineering
%V 23
%N 2
%P 234-245
%@ 2095-9184
%D 2022
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.2000358

T1 - Novel robust simultaneous localization and mapping for long-term autonomous robots
A1 - Wei WEI
A1 - Xiaorui ZHU
A1 - Yi WANG
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 23
IS - 2
SP - 234
EP - 245
%@ 2095-9184
Y1 - 2022
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/FITEE.2000358

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.


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。


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


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