RESUMEN
LiDAR offers a wide range of uses in autonomous driving, remote sensing, urban planning, and other areas. The laser 3D point cloud acquired by LiDAR typically encounters issues during registration, including laser speckle noise, Gaussian noise, data loss, and data disorder. This work suggests a novel Student's t-distribution point cloud registration algorithm based on the local features of point clouds to address these issues. The approach uses Student's t-distribution mixture model (SMM) to generate the probability distribution of point cloud registration, which can accurately describe the data distribution, in order to tackle the problem of the missing laser 3D point cloud data and data disorder. Owing to the disparity in the point cloud registration task, a full-rank covariance matrix is built based on the local features of the point cloud during the objective function design process. The combined penalty of point-to-point and point-to-plane distance is then added to the objective function adaptively. Simultaneously, by analyzing the imaging characteristics of LiDAR, according to the influence of the laser waveform and detector on the LiDAR imaging, the composite weight coefficient is added to improve the pertinence of the algorithm. Based on the public dataset and the laser 3D point cloud dataset acquired in the laboratory, the experimental findings demonstrate that the proposed algorithm has high practicability and dependability and outperforms the five comparison algorithms in terms of accuracy and robustness.
RESUMEN
A method for enhancing the resolution of 3D imaging reconstruction by employing the polarization modulation of electro-optical crystals is proposed. This technique utilizes two polarizers oriented perpendicular to each other along with an electro-optical modulation crystal to achieve high repetition frequency and narrow pulse width gating. By varying the modulation time series of the electro-optical crystal, three-dimensional gray images of the laser at different distances are acquired, and the three-dimensional information of the target is reconstructed using the range energy recovery algorithm. This 3D imaging system can be implemented with large area detectors, independent of the an Intensified Charge-Coupled Device (ICCD) manufacturing process, resulting in improved lateral resolution. Experimental results demonstrate that when imaging a target at the distance of 20 m, the lateral resolution within the region of interest is 2560 × 2160, with a root mean square error of 3.2 cm.
RESUMEN
In order to achieve wide field-of-view, high-resolution LIDAR, a gating imaging structure combining an electro-optic crystal and an electron multiplication CCD is constructed. According to the index ellipsoid theory, a 3D ray tracing model is established to explore the principle of electro-optic modulation. The field-of-view and interference intensity distribution of the LiNbO3(LN) crystal electro-optic modulation are studied by using the proposed model. In order to solve the problem that the interference light intensity at the edge of the field-of-view of crystal electro-optic modulation is not homogeneous, we study and propose an electro-optic modulation aberration correction algorithm based on phase difference compensation. The corrected interference light intensity at the edge of the field-of-view increased from 0.87 to 0.94. Finally, a LIDAR imaging simulation system is established to image the target at a distance of 2000 m. The results show that under the condition of a 1° wide field-of-view, the imaging accuracy of the system is 5 mm, and the average imaging error introduced by the crystal electro-optic modulation is less than 2 cm.