Fast rotation search for real-time interactive point cloud registration

Abstract

Figure 1: A set of partially overlapping 3D laser scans (viewed from the top) from an underground mine and their correct registration. AbstractOur 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 alignment is unavailable. Here, we propose an interactive user-assisted point cloud registration system. Guided by the system, the user's role is simply to identify and search for overlapping regions across the point clouds. Specifically, given two point sets, the user clicks on a point in one set, then simply hovers the mouse on the other set to find a matching point. Each mouse position gives rise to a translation, and our system instantly optimises the rotation that aligns the point clouds.Assuming that each individual point set is horizontally levelled with the ground by the level compensator on the LIDAR device, given a candidate 3D translation only one angular parameter needs to be estimated to rotationally align two 3D point sets. We propose a fast rotation search algorithm that delivers globally optimal results in real time. Our method conducts branch-and-bound optimisation with a novel bounding function whose evaluation amounts to simple sorted array operations. This provides smooth and accurate feedback to the user's search for overlapping regions. Our system is intuitive and helps to accelerate the registration of multiple scans.

Maximum Consensus Localization Using Lidar Sensors

“…For example, Branch-and-Bound (BnB)-, tree-, and exhaustive search were reported in the literature. (Li, 2009) and (Zheng et al, 2011) as well as (Breuel, 1992), (Chin et al, 2014), and(Parra Bustos et al, 2016) introduced BnB algorithms. (Olsson et al, 2008) and (Enqvist et al, 2012) pursued exhaustive search by enumeration, while introduced a tree search strategy.…”

“…Point cloud registration is a very well-researched topic. For example, (Chen et al, 1999) and (Aiger et al, 2008) use randomized heuristics, (Li and Hartley, 2007) and (Yang et al, 2013) are using maximum likelihood frameworks, and (Chin et al, 2014) and (Parra Bustos et al, 2016) are using a geometric matching criterion, striving for globally optimal point cloud registration using BnB. The most common algorithm used for point cloud registration is arguably the Iterative Closest Point (ICP) algorithm (Besl and McKay, 1992), which approaches intractability by using two alternating phases, (i) finding correspondences and (ii) minimizing the residuals.…”

EROS-DOCK: protein–protein docking using exhaustive branch-and-bound rotational search

“…In other words, coordinate distances in the quaternion πball representation provide upper bounds for the corresponding rotational distances in Euler angle rotation space. This important property has been exploited previously to develop efficient branch-and-bound based search algorithms for the problem of finding the optimal registration of two 3D point clouds (Chin et al, 2014;Bustos et al, 2014) which is a common problem in computer vision. In this paper, we apply for the first time a similar branch-and-bound based rotational search to the 6D rigid-body protein docking problem.…”