LAUSR

Point cloud registration is an essential technique for many tasks including autonomous navigation, augmented/virtual reality and scene reconstruction. In man-made environments, abundant plane features with structural regularities are commonly seen, which is actually beneficial for point cloud registration. This paper presents an accurate and robust registration method that formulates the registration problem as a maximum likelihood estimation (MLE) problem where the useful structure information is also leveraged. To align two point sets efficiently, one point set is first represented as a Gaussian mixture models (GMM) tree, and then the Manhattan frame (MF) with dominant planes are estimated and the degenerated nodes corresponding to the dominant planes in the GMM tree are clustered to embody the structure information. The proposed MLE problem is solved by an Expectation Maximization (EM) framework. In the E step, the probabilistic data association between the GMM and another point set is first estimated and then refined by aligning the MF and planes. In the M step, an optimization problem using Mahalanobis distance is solved. Experimental results on both indoor room and hybrid man-made environments show that the proposed method not only achieves a good balance between accuracy and speed, but also provides robust and accurate performance in sequence registration process. The source code is available on our Github.11https://github.com/liu-jc18/StructRegistration.git