Rapid Target Extraction in LiDAR Sensing and Its Application in Rocket Launch Phase Measurement
Source abstract
Abstract
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.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Advanced Optical Sensing Technologies · Space Exploration and Technology · Planetary Science and Exploration
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Xi’an Institute of Optics and Precision Mechanics of CAS, Xi’an 710119, China
School of Optoelectronics, University of Chinese Academy of Sciences, Beijing 100049, China
Key Laboratory of Space Precision Measurement Technology, Chinese Academy of Sciences, Xi’an 710119, China
Funding
The Youth Innovation Promotion Association CAS Fund Project · Key Laboratory of Space Precision Measurement Technology CAS Fund Project · Key Laboratory Fund of the Chinese Academy of Sciences
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