Abstract
We report on a self-adaptive waveform centroid algorithm that combines the selection of double-scale data and the intensity-weighted (DSIW) method for accurate LiDAR distance–intensity imaging. A time window is set to adaptively select the effective data. At the same time, the intensity-weighted method can reduce the influence of sharp noise on the calculation. The horizontal and vertical coordinates of the centroid point obtained by the proposed algorithm are utilized to record the distance and echo intensity information, respectively. The proposed algorithm was experimentally tested, achieving an average ranging error of less than 0.3 ns under the various noise conditions in the listed tests, thus exerting better precision compared to the digital constant fraction discriminator (DCFD) algorithm, peak (PK) algorithm, Gauss fitting (GF) algorithm, and traditional waveform centroid (TC) algorithm. Furthermore, the proposed algorithm is fairly robust, with remarkably successful ranging rates of above 97% in all tests in this paper. Furthermore, the laser echo intensity measured by the proposed algorithm was proved to be robust to noise and to work in accordance with the transmission characteristics of LiDAR. Finally, we provide a distance–intensity point cloud image calibrated by our algorithm. The empirical findings in this study provide a new understanding of using LiDAR to draw multi-dimensional point cloud images.
Highlights
Since its emergence, LiDAR has been rapidly applied to the acquisition of threedimensional space information, providing a new technical solution for three-dimensional modeling of cities [1], exploration and detection of geology and roads [2,3], autonomous driving and unmanned driving of vehicles [4,5], etc
The backscattered optical power is internally converted into a voltage, sampled by an analog-to-digital converter, and, transformed into a Digital Number (DN), that is, a scaled integer value called the “intensity” [11]
Ωs where Ωs is the solid angle of the backscattering direction, ρ is the target biconical reflectance (BRF) at the laser wavelength used, Ai is the area of the spot illuminated at the target, and θi is the angle of incidence
Summary
LiDAR has been rapidly applied to the acquisition of threedimensional space information, providing a new technical solution for three-dimensional modeling of cities [1], exploration and detection of geology and roads [2,3], autonomous driving and unmanned driving of vehicles [4,5], etc. The 3D point cloud data acquired by. Traditional applications of 3D LiDAR often focus on the spatial information of the targets contained in the point cloud data, making the application of LiDAR data relatively limited. Several studies have shown that LiDAR intensity data have strong application. Blackburn et al [16] measured the peak value as the intensity of the laser pulse and inferred that it is independent of the model of the electron dynamics. Most related research is based on intensity data from commercial LiDAR or terrestrial laser scanning (TLS) sensors. No research was found that surveyed the robustness of ranging and the representation of digital LiDAR intensity data with the impact of noise
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