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.


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    Title :

    Fast Iterative Closest Point framework for 3D LIDAR data in intelligent vehicle


    Contributors:


    Publication date :

    2012-06-01


    Size :

    879988 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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