A Radar Linear Feature Fitting Algorithm Combining Adaptive Clustering and Corner Detection Operator

• Published in: Journal of sensors Vol. 2023; no. 1
• Main Authors: Liu, Yiting, Sui, Lianjie, Li, Peijuan, Zhang, Lei, Wu, Qingzheng, Du, Junfeng, Liu, Yawen, Yu, Hanqi
• Format: Journal Article
• Language: English
• Published: New York Hindawi 24.02.2023; John Wiley & Sons, Inc
• Subjects: Accuracy; Adaptive algorithms; Algorithms; Autonomous navigation; Cartography; Clustering; Corner detection; Corners; Data points; Data processing; Feature extraction; Iterative methods; Lasers; Lidar; Mathematical analysis; Radar data; Real time; Robots; Simultaneous localization and mapping; Wavelet transforms
• ISSN: 1687-725X; 1687-7268; 1687-7268
• DOI: 10.1155/2023/6991467

The precise environmental parameters derived from laser radar scan data can significantly accelerate the process of real-time localization and map-matching technique. One of the research directions is autonomous navigation algorithm based on LiDAR slam. LiDAR has the advantage of having a wide range of accuracy and distance. However, due to the limited amount of LiDAR data available and the influence of sensor noise, it is easy to run into issues such as low accuracy of robot map construction or large positioning errors. At the moment, most of feature extraction algorithms employ an iterative calculation method with high computational complexity and a large amount of computation. Furthermore, due to the dependence of the fixed separation threshold, the algorithms for extracting the linear features of laser radar data are typically undersegment and oversegment. As a result, this paper proposes a radar linear feature fitting algorithm that combines adaptive clustering and corner detection operators. First, bilateral filtering is used to reduce noise and remove invalid data points. Second, the LiDAR data points are classified using adaptive threshold clustering of distance and density. The corner detection operator is applied to the classified data points to determine all possible corners then. Finally, the least square method is used to linearly fit each class and the identified corners within each class. The simulation and experimental results demonstrate that this method avoids the influence of noise points and a fixed segmentation threshold on corner point extraction effectively. The standard variance of length is 9.41×10−5m2 for corner feature extraction and localization in the dataset Cartographer ROS 2D Laser SLAM at Deutsches Museum. When compared to PDBS (point distance based methods) and IEPF (iterative end point fit), only about half the time is used, the accuracy of partition processing is improved by 11.6%, and the accuracy of corner detection is improved by 20.1%. The proposed algorithm can extract the corner features of data frames and linear positioning through experimental verification accurately. The features of the laser scan data that fit are more realistic. It has higher calculation efficiency and position accuracy. It ensures real-time mobile robot map construction and is appropriate for autonomous robot map algorithms developed in embedded systems.