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; 朱晓蕊; 王毅

doi:10.1631/FITEE.2000358

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用于长期自主机器人的新型鲁棒同时定位与建图方法

常规文章 | Updated：2022-04-19

- 用于长期自主机器人的新型鲁棒同时定位与建图方法
  Enhanced Publication
- Novel robust simultaneous localization and mapping for long-term autonomous robots
- 信息与电子工程前沿（英文） 2022年23卷第2期页码：234-245
- Affiliations：
  
  1.School of Mechanical Engineering and Automation, Harbin Institute of Technology, Shenzhen518055, China
  2.Zhuhai Big Data Research Institute, Zhuhai519000, China
- Author bio：
  
  E-mail: weirui9003@gmail.com;
  ‡Corresponding author
  E-mail: wangyi601@aliyun.com
- Funds：
  
  National Key R&D Program of China(2018YFB1305500);National Natural Science Foundation of China(U1813219)
- DOI：10.1631/FITEE.2000358
  中图分类号： TP399
- 收稿：2020-07-18，
  
  录用：2020-11-18，
  
  网络出版：2022-02-05，
  
  纸质出版：2022-02-0
- Accepted：
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魏伟, 朱晓蕊, 王毅. 用于长期自主机器人的新型鲁棒同时定位与建图方法[J]. 信息与电子工程前沿（英文）, 2022,23(2):234-245.

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.
魏伟, 朱晓蕊, 王毅. 用于长期自主机器人的新型鲁棒同时定位与建图方法[J]. 信息与电子工程前沿（英文）, 2022,23(2):234-245. DOI： 10.1631/FITEE.2000358.

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. DOI： 10.1631/FITEE.2000358.

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。

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.

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