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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    Titel :

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


    Beteiligte:
    Choi, Won-Seok (Autor:in) / Kim, Yang-Shin (Autor:in) / Oh, Se-Young (Autor:in) / Lee, Jeihun (Autor:in)


    Erscheinungsdatum :

    01.06.2012


    Format / Umfang :

    879988 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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