iRotAvg incrementally solves rotation averaging. iRotAvg is the optimisation core of L-infinity SLAM.
Registering two 3D point clouds involves estimating the rigid transform that brings the two point clouds into alignment. Recently there has been a surge of interest in using branch-and-bound (BnB) optimisation for point cloud registration. While BnB …
In this project, we present an algorithm called guaranteed outlier removal (GORE) to safely and efficiently remove outliers. GORE is presented in the context of rotation search and point cloud registration. Source code of GORE is available for both problems.
GORE for Rotation Search Rotation search has become a core routine for solving many computer vision problems. The aim is to rotationally align two input point sets with correspondences. Recently, there is significant interest in developing globally optimal rotation search algorithms.
Fast rotation search with stereographic projections for 3D registration First we present a globally optimal 3D rotation search method. Based on branch and bound, bound evaluations are accelerated by using stereographic projections to precompute and index all possible point matches in spatial R-trees.
Also we present a global 6 DoF point cloud registration algorithm which uses our rotation search algorithm as a kernel in a nested branch and bound design.
Recently there has been a surge of interest to use branch-and-bound (bnb) optimisation for 3D point cloud registration. While bnb guarantees globally optimal solutions, it is usually too slow to be practical. A fundamental source of difficulty is the …
Our goal is the registration of multiple 3D point clouds obtained from LIDAR scans of underground mines. Such a capability is crucial to the surveying and planning operations in mining. Often, the point clouds only partially overlap and initial …