Delaunay Walk for Fast Nearest Neighbor: Accelerating Correspondence Matching for ICP

Authors

James D. Anderson, Wright State University - Main Campus
Ryan M. Raettig
Josh Larson
Scott L. Nykl
Clark N. Taylor
Thomas Wischgoll, Wright State University - Main CampusFollow

Document Type

Article

Publication Date

3-1-2022

Abstract

Point set registration algorithms such as Iterative Closest Point (ICP) are commonly utilized in time-constrained environments like robotics. Finding the nearest neighbor of a point in a reference 3D point set is a common operation in ICP and frequently consumes at least 90% of the computation time. We introduce a novel approach to performing the distance-based nearest neighbor step based on Delaunay triangulation. This greedy algorithm finds the nearest neighbor of a query point by traversing the edges of the Delaunay triangulation created from a reference 3D point set. Our work integrates the Delaunay traversal into the correspondences search of ICP and exploits the iterative aspect of ICP by caching previous correspondences to expedite each iteration. An algorithmic analysis and comparison is conducted showing an order of magnitude speedup for both serial and vector processor implementation.

Comments

This work is licensed under a Creative Commons Attribution 4.0 International License.

Repository Citation

Anderson, J. D., Raettig, R. M., Larson, J., Nykl, S. L., Taylor, C. N., & Wischgoll, T. (2022). Delaunay Walk for Fast Nearest Neighbor: Accelerating Correspondence Matching for ICP. Machine Vision and Applications, 33 (2), 31, 1-13.
https://corescholar.libraries.wright.edu/cse/615

DOI

10.1007/s00138-022-01279-w