Globally consistent registration of terrestrial laser scans via graph optimization
METADATA ONLY
Loading...
Author / Creator
Date
2015
Publication Type
Journal Article
ETH Bibliography
yes
Citations
Altmetric
METADATA ONLY
Data
Rights / License
Abstract
In this paper we present a framework for the automatic registration of multiple terrestrial laser scans. The proposed method can handle arbitrary point clouds with reasonable pairwise overlap, without knowledge about their initial orientation and without the need for artificial markers or other specific objects. The framework is divided into a coarse and a fine registration part, which each start with pairwise registration and then enforce consistent global alignment across all scans. While we put forward a complete, functional registration system, the novel contribution of the paper lies in the coarse global alignment step. Merging multiple scans into a consistent network creates loops along which the relative transformations must add up. We pose the task of finding a global alignment as picking the best candidates from a set of putative pairwise registrations, such that they satisfy the loop constraints. This yields a discrete optimization problem that can be solved efficiently with modern combinatorial methods. Having found a coarse global alignment in this way, the framework proceeds by pairwise refinement with standard ICP, followed by global refinement to evenly spread the residual errors. The framework was tested on six challenging, real-world datasets. The discrete global alignment step effectively detects, removes and corrects failures of the pairwise registration procedure, finally producing a globally consistent coarse scan network which can be used as initial guess for the highly non-convex refinement. Our overall system reaches success rates close to 100% at acceptable runtimes <1 h, even in challenging conditions such as scanning in the forest.
Permanent link
Publication status
published
Editor
Book title
Journal / series
Volume
109
Pages / Article No.
126 - 138
Publisher
Elsevier
Event
Edition / version
Methods
Geographic location
Date collected
Date created
Subject
Point cloud registration; Terrestrial laser scanning; Geometric matching; Global consistency; Loop closure; Energy minimization
Organisational unit
03886 - Schindler, Konrad / Schindler, Konrad
Notes
Received 10 June 2015, Received in revised form 18 August 2015, Accepted 25 August 2015.