Ground Target Detection in LiDAR Point Clouds using AdaBoost

Although substantial progress has been made in objects detecting in point clouds, the performance of most methods is limited by the accuracy of segmentation. However, accurate segmentation in complex scene is still an open problem. This paper presents a novel rotation-invariant method for object detection. It uses Adaboost to train a detector for exhaustively scaning and testing of the point cloud scene. First, to address the rotation-sensitive problem of 3D Harr-like features, we use positive training samples obtained from multiple viewpoints of the object. Then, false alarm is reduced using the prior knowledge that the confidence of false alarm distributes sparsely in the space. Experimental results demonstrate that the proposed method achieves a high recall on point clouds obtained from multiple viewpoints of the object at the low false alarm.