Cached k-d tree search for ICP algorithms

Authors:
Andreas Nuchter;Kai Lingemann;Joachim Hertzberg
Affiliations:
University of Osnabruck, Germany;University of Osnabruck, Germany;University of Osnabruck, Germany
Venue:
3DIM '07 Proceedings of the Sixth International Conference on 3-D Digital Imaging and Modeling
Year:
2007

Citing 0
Cited 7

Towards 3D Point cloud based object maps for household environments

Robotics and Autonomous Systems
Fast point feature histograms (FPFH) for 3D registration

ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Affine iterative closest point algorithm for point set registration

Pattern Recognition Letters
Study of parameterizations for the rigid body transformations of the scan registration problem

Computer Vision and Image Understanding
Real time surface registration for PET motion tracking

SCIA'11 Proceedings of the 17th Scandinavian conference on Image analysis
Semantic simulation engine for supervision of mobile robotic system

ICCCI'11 Proceedings of the Third international conference on Computational collective intelligence: technologies and applications - Volume Part II
A robust and fast method for 6DoF motion estimation from generalized 3D data

Autonomous Robots

Quantified Score

Hi-index	0.00

Visualization

Abstract

The ICP (Iterative Closest Point) algorithm is the de facto standard for geometric alignment of three-dimensional models when an initial relative pose estimate is available. The basis of ICP is the search for closest points. Since the development of ICP, k-d trees have been used to accelerate the search. This paper presents a novel search procedure, namely cached k-d trees, exploiting iterative behavior of the ICP algorithm. It results in a significant speedup of about 50% as we show in an evaluation using different data sets.