An improved SLAM algorithm for substation inspection robot based on the fusion of IMU and visual information

Q2 Energy Energy Informatics Pub Date : 2024-09-27 DOI:10.1186/s42162-024-00390-8
Ping Wang, Chuanxue Li, Fangkai Cai, Li Zheng
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Abstract

In the past, manual inspection was often used for equipment inspection in indoor environments such as substation rooms and chemical plant rooms. This way often accompanies high labor intensity, low inspection efficiency, and low safety, which is difficult to meet the increasingly stringent requirements of indoor equipment operation and maintenance management. For dealing with these issues, a VIORB-SLAM2 algorithm based on the integration of IMU and visual information, was proposed by this paper. Firstly, the IMU data and image data were integrated to restore scale information of cameras, and then an error function was established to enhance the algorithm’s robustness. Secondly, in order to improve the accuracy of the algorithm, the random sampling consensus method was used to eliminate the wrong matching points in feature point matching, and the normalized cross-correlation matching was employed to constrain key frame matching conditions. Finally, through the iterative closest point method to stitch the point clouds, a dense map for navigation was constructed. The experimental results show that the algorithm designed by this paper has solved the shortcomings of applying the ORB-SLAM2 algorithm to indoor inspection robots while achieving high positioning accuracy, which can be combined with other algorithms in the field of artificial intelligence for object detection and semantic map construction in the future.

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基于 IMU 和视觉信息融合的变电站巡检机器人 SLAM 改进算法
过去,在变电站房、化工厂房等室内环境的设备巡检中,往往采用人工巡检的方式。这种方式往往伴随着劳动强度大、巡检效率低、安全性差等问题,难以满足日益严格的室内设备运行维护管理要求。针对这些问题,本文提出了一种基于 IMU 和视觉信息集成的 VIORB-SLAM2 算法。首先,整合 IMU 数据和图像数据,还原摄像机的比例信息,然后建立误差函数,增强算法的鲁棒性。其次,为了提高算法的准确性,采用随机抽样共识法消除特征点匹配中的错误匹配点,并采用归一化交叉相关匹配来约束关键帧匹配条件。最后,通过迭代最近点法拼接点云,构建了用于导航的密集地图。实验结果表明,本文设计的算法解决了将ORB-SLAM2算法应用于室内巡检机器人的不足,同时实现了较高的定位精度,未来可与人工智能领域的其他算法相结合,用于物体检测和语义地图构建。
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来源期刊
Energy Informatics
Energy Informatics Computer Science-Computer Networks and Communications
CiteScore
5.50
自引率
0.00%
发文量
34
审稿时长
5 weeks
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