LPIR-GSeg: LiDAR Projection Image-Based and Region-Wise Ground Segmentation in Urban Environment

Fast and accurate ground segmentation is an essential task for various applications, such as point cloud clustering, 3-D object segmentation, and simultaneous localization and mapping (SLAM). To enhance both accuracy and efficiency, our method avoids the traditional plane-fitting approach for 3-D point clouds. We propose a light detection and ranging (LiDAR) projection image-based and region-wise ground segmentation method in urban road scenarios named LPIR-GSeg. We calibrate the mounted angle between the LiDAR and the horizontal plane to correct the heights of 3-D points. For coarse segmentation, we encode the point cloud into a concentric zone model representation and find the corresponding grid for each 3-D point. The heights of the grids will be used for fast ground segmentation, which is used as an initial value for the fine segmentation. To improve the accuracy, we project the point cloud into various cylindrical images based on the LiDAR projection model. Then, a region-growing algorithm based on the projection images is applied to achieve refined ground segmentation. Comprehensive experiments were conducted with a real-world platform and the SemanticKITTI datasets. Our proposed method achieves a precision of 95.93% and a recall of 92.20% on the SemanticKITTI dataset.