Abstract. Point cloud registration sits at the core of many important
and challenging 3D perception problems including autonomous navigation, SLAM, object/scene recognition, and augmented reality. In this
paper, we present a new registration algorithm that is able to achieve
state-of-the-art speed and accuracy through its use of a Hierarchical
Gaussian Mixture representation. Our method, Hierarchical Gaussian
Mixture Registration (HGMR), constructs a top-down multi-scale representation of point cloud data by recursively running many small-scale
data likelihood segmentations in parallel on a GPU. We leverage the
resulting representation using a novel optimization criterion that adaptively finds the best scale to perform data association between spatial
subsets of point cloud data. Compared to previous Iterative Closest Point
and GMM-based techniques, our tree-based point association algorithm
performs data association in logarithmic-time while dynamically adjusting the level of detail to best match the complexity and spatial distribution characteristics of local scene geometry. In addition, unlike other
GMM methods that restrict covariances to be isotropic, our new PCAbased optimization criterion well-approximates the true MLE solution
even when fully anisotropic Gaussian covariances are used. Efficient data
association, multi-scale adaptability, and a robust MLE approximation
produce an algorithm that is up to an order of magnitude both faster
and more accurate than current state-of-the-art on a wide variety of 3D
datasets captured from LiDAR to structured light