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SLAM Algorithm for Mobile Robots Based on Improved LVI-SAM in Complex Environments

文献类型： 外文期刊

作者： Wang, Wenfeng 1 ; Li, Haiyuan 1 ; Yu, Haiming 1 ; Xie, Qiuju 1 ; Dong, Jie 1 ; Sun, Xiaofei 1 ; Liu, Honggui 5 ; Sun, Congcong 6 ; Li, Bin 7 ; Zheng, Fang 3 ;

作者机构： 1.Northeast Agr Univ, Coll Elect Engn & Informat, Harbin 150030, Peoples R China

2.Minist Agr & Rural Affairs, Key Lab Equipment & Informatizat Environm Control, Hangzhou 310058, Peoples R China

3.Minist Agr & Rural Affairs, Key Lab Smart Farming Technol Agr Anim, Wuhan 430070, Peoples R China

4.Minist Educ, Engn Res Ctr Pig Intelligent Breeding & Farming No, Harbin 150030, Peoples R China

5.Northeast Agr Univ, Coll Anim Sci & Technol, Harbin 150030, Peoples R China

6.Wageningen Univ, Agr Biosyst Engn Grp, NL-6700 AA Wageningen, Netherlands

7.Beijing Acad Agr & Forestry Sci, Intelligent Equipment Res Ctr, Beijing 100097, Peoples R China

8.Huazhong Agr Univ, Coll Informat, Wuhan 430070, Peoples R China

关键词： multi-sensor fusion; SLAM; feature extraction; loop-closure detection; navigation

期刊名称：SENSORS （ 影响因子：3.5； 五年影响因子：3.7 ）

ISSN：

年卷期： 2024 年 24 卷 22 期

页码：

收录情况： SCI

摘要： The foundation of robot autonomous movement is to quickly grasp the position and surroundings of the robot, which SLAM technology provides important support for. Due to the complex and dynamic environments, single-sensor SLAM methods often have the problem of degeneracy. In this paper, a multi-sensor fusion SLAM method based on the LVI-SAM framework was proposed. First of all, the state-of-the-art feature detection algorithm SuperPoint is used to extract the feature points from a visual-inertial system, enhancing the detection ability of feature points in complex scenarios. In addition, to improve the performance of loop-closure detection in complex scenarios, scan context is used to optimize the loop-closure detection. Ultimately, the experiment results show that the RMSE of the trajectory under the 05 sequence from the KITTI dataset and the Street07 sequence from the M2DGR dataset are reduced by 12% and 11%, respectively, compared to LVI-SAM. In simulated complex environments of animal farms, the error of this method at the starting and ending points of the trajectory is less than that of LVI-SAM, as well. All these experimental comparison results prove that the method proposed in this paper can achieve higher precision and robustness performance in localization and mapping within complex environments of animal farms.

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