Aiming at the problem of reduced positioning accuracy of lightweight and ground-optimized light detection and ranging odometry and mapping(LeGO-LOAM)algorithm caused by inaccurate ground segmentation in outdoor complex scenes,the paper proposed a multi-threaded ground segmentation algorithm based on improved random sample consensus(RANSAC):compared with the traditional RANSAC algorithm,the iterative method of randomly selecting seed points from all the raw data was abandoned to fit the ground model,but the point cloud elevation,curvature and other point feature information were used to select all seed points that are less than the elevation,curvature and other thresholds to construct a seed point set,and whether multi-threaded processing was needed was determined based on the number of seed points in the seed point set;then,based on the judgment results,a subset of seed points from the seed point set was seletct for ground fitting;finally,the number of point clouds contained in each ground model was compared to obtain the optimal ground model parameters and ground point cloud set;the improvement of ground segmentation accuracy could effectively reduce the positioning error of the LeGO-LOAM algorithm.Experimental results showed that the proposed ground segmentation algorithm would perform better in outdoor complex scenes,with fewer noise points;moreover,compared with the original LeGO-LOAM algorithm,the improved algorithm could reduce the positioning error to 3.73 m and decrease the root mean square error in the plane by 20.8%.
lightweight and ground-optimized light detection and ranging odometry and mapping(LeGO-LOAM)random sample consensus(RANSAC)ground segmentationoutdoor positioning
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