Rapid Target Extraction in LiDAR Sensing and Its Application in Rocket Launch Phase Measurement

The paper presents a fast method for 3D point cloud target extraction, addressing the challenge of time-consuming processing in LiDAR-based 3D point cloud data. The method begins with the acquisition of environmental 3D point cloud data using LiDAR, which is then projected onto a 2D cylindrical map. We propose a method for rapid target extraction from LiDAR-based 3D point cloud data, which includes key steps such as projection into 2D space, image processing for segmentation, and target extraction. A mapping matrix between the 2D grayscale image and the cylindrical projection is derived through Gaussian elimination. A target backtracking search algorithm is used to map the extracted target region back to the original 3D point cloud, enabling precise extraction of the 3D target points. Near-field experiments using hybrid solid-state LiDAR demonstrate the method’s effectiveness, requiring only 0.53 s to extract 3D target point clouds from datasets containing hundreds of thousands of points. Further, far-field rocket launch experiments show that the method can extract target point clouds within 158 milliseconds, with measured positional offsets of 0.2159 m and 0.1911 m as the rocket moves away from the launch tower.