Delaunay walk for fast nearest neighbor: accelerating correspondence matching for ICP

Journal Article · 14 February 2022 · Machine Vision and Applications

DOI: https://doi.org/10.1007/s00138-022-01279-w · OSTI ID:1981469

Anderson, James D.  ^[1]; Raettig, Ryan M. ^[2]; Larson, Josh ^[2]; Nykl, Scott L. ^[2]; Taylor, Clark N. ^[2]; Wischgoll, Thomas ^[1]

---

1. Wright State University, Dayton, OH (United States)
2. Air Force Institute of Technology, Wright-Patterson AFB, OH (United States)

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.

• Research Organization: Oak Ridge Institute for Science and Education (ORISE), Oak Ridge, TN (United States)
• Sponsoring Organization: USDOE Office of Science (SC)
• Grant/Contract Number: SC0014664
• OSTI ID: 1981469
• Journal Information: Machine Vision and Applications, Journal Name: Machine Vision and Applications Journal Issue: 2 Vol. 33; ISSN 0932-8092
• Publisher: SpringerCopyright Statement
• Country of Publication: United States
• Language: English