Abstract:

For the lidar automatic target recognition purpose, a method based on point cloudsmodel matching is proposed in this paper. The point clouds are  projected to 3views and binary images are created after binarization. The Sobel operator and Hough transform are applied to the binary images to extract the silhouette boundary and line parameters, then the bounding rectangles are  extracted with the restriction of projected point clouds’ silhouette information, thus the target’s orientation and geometric feature are obtained. Taking the minimum of the mean Euclidean distance between point clouds and facets of the CAD model as the optimization target, utilizing the unit quaternion method for rigid transform calculation, the matching between point clouds and the CAD model is realized by using iteration, and the recognition process is finished by taking the normalized mean Euclidean distance as similarity metric. Point clouds of five kinds of ground armored vehicles in different lidar viewing angles are  used to fulfill the recognition experiment. The statistical results show that the target’s class recognition rate is 100% and the type recognition rate is above 91%. So this method performs well on recognition and has better application prospect.

Key words: signal processing;lidar;target recognition;orientation estimation;point cloudsmodel matching