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Improved LeGO-LOAM method based on outlier points elimination

2023; Elsevier BV; Volume: 214; Linguagem: Inglês

10.1016/j.measurement.2023.112767

1873-412X

Ziwei Wang, Guangjun Liu,

3D Surveying and Cultural Heritage

Based on the analysis of the shortcomings of Lightweight and Ground-Optimized Lidar Odometry and Mapping (LeGO-LOAM) algorithm, an improved LeGO-LOAM method based on outlier points elimination (LeGO-LOAM-OE) was proposed. The original point cloud data generated by Light Detection and Ranging (LiDAR) was analyzed and the four types of outlier points were eliminated, which made the extracted feature points more effective and improved the accuracy of pose estimation. In ground segmentation, the levelness of the ground is constrained so that the extracted ground points are more abundant and accurate. In dynamic point elimination, dynamic objects are eliminated by the moving distance of the same cluster objects in adjacent frames, and the calculation method is simple. The simulation results show that the trajectory accuracy generated by LeGO-LOAM-OE is higher than that generated by LeGO-LOAM. Compared with LeGO-LOAM, LeGO-LOAM-OE can effectively reduce the maximum error and average error of three axes. The total maximum error is reduced by 14.3%, and the total mean error is reduced by 7.6%. LeGO-LOAM-OE had the most significant effect on reducing the Y-axis error (vertical direction), with an average error reduction of 8.5%. An experimental platform was built to test the positioning accuracy and mapping effect of the proposed algorithm in an outdoor environment. Experimental results showed that the proposed method has high accuracy in both online and offline simultaneous localization and mapping (SLAM). The maximum error of the calculated position is less than 1 m, and the average error is less than 0.1 m. Moreover, the generated point cloud map can highly reproduce the environment without deviation.