The Iterative Closest Point (ICP) algorithm is one of the most popular methods for geometric alignment of 3-dimensional data points. We focus on how to make it faster for 3D range scanner in intelligent vehicle. The ICP algorithm mainly consists of two parts: nearest neighbor search and estimation of transformation between two data sets. The former is the most time consuming process. Many variants of the k-d trees have been introduced to accelerate the search. This paper presents a remarkably efficient search procedure, exploiting two concepts of approximate nearest neighbor and local search. Consequently, the proposed algorithm is about 24 times faster than the standard k-d tree.
Fast Iterative Closest Point framework for 3D LIDAR data in intelligent vehicle
2012 IEEE Intelligent Vehicles Symposium ; 1029-1034
01.06.2012
879988 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
Fast Iterative Closet Point Framework for 3D LIDAR Data in Intelligent Vehicle
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