'Institute of Electrical and Electronics Engineers (IEEE)'
Abstract
Point cloud registration is widely used in the research of intelligent vehicles, typical problems include map matching, visual odometer, pose estimation, etc. This paper proposes a deep learning-based registration method that can input point clouds directly, thereby preventing information loss of preprocessing needed by alternative deep-learning approaches. Our network, named DPFNet (Direct Point Feature Net), gradually downsamples the point cloud and aggregates points around determined reference points to formulate local features automatically. This is facilitated by a novel convolution-like operator and a novel loss function. The points in the point cloud are mapped to a high dimensional embedding through the designed deep neural network, where every embedding reflects the local feature of a specific spatial area. Based on the embedding features, correspondences between points can be estimated robustly and the registration between the point clouds can be obtained using an external geometric optimization algorithm. Experimental results on open benchmarks validate the proposed method and show that its performance is favourable over several baseline methods. Specifically, we test the proposed algorithm on KITTI benchmark, which shows its potential in tasks of intelligent vehicles, e.g., map matching, visual or LiDAR odometer
