LiDAR Point Cloud Object Recognition Method via Intensity Image Compensation

LiDAR point cloud object recognition plays an important role in robotics, remote sensing, and automatic driving. However, it is difficult to fully represent the feature information only by using information. To address this challenge, we proposed a method that uses intensity image compensation, which highly descriptive computationally efficient. First, constructed local reference frame for cloud. Second, calculate deviation angle between normal vector neighborhood of Third, extracted contour from corresponding cloud, carried out Discrete Fourier Transform on distance sequence barycenter each contour, took obtained result as object. Finally, repeated above steps existing prior data marked results build model library. We can recognize unknown calculating be recognized matching with rigorously tested avalanche photon diode array compared those four other methods. The experimental show superior comparison terms description computational efficiency meet needs practical applications.