Automatic Registration Method for TLS LiDAR Data and Image-Based Reconstructed Data

Point clouds registration is an important research topic in the field of data fusion from camera and light detection and ranging (LiDAR). In this letter, a new registration method, fast multiscale registration (FMSR), takes the scale factor into account and is proposed for the registration of two point clouds obtained from camera and LiDAR. An adaptive-scale keypoint quality algorithm was used to detect and match keypoints, which were input to the coarse registration process to improve the coarse registration accuracy. A new heuristic criterion was also proposed for fine registration, which avoids falling into the local minima. Furthermore, to increase efficiency of fine registration, the k-nearest neighbors algorithm was selected to directly search the optimal matching from the raw point clouds without triangulating point clouds into mesh. The FMSR method is highly precise, insensitive to outliers, and relatively efficient. Experimental results showed that the root-mean-square error of the registration was approximately 0.2 m when the size of the object was about 20.3 m × 7.85 m × 26.56 m, the total number of matched points was 12 789, and the execution time was approximately 2.1 s, indicating that the proposed method resulted in improved accuracy and efficiency of registration.