Fast Iterative Closest Point Framework for 3D LIDAR data in Intelligent Vehicle

被引：0
|
作者：
Choi, Won-Seok [1 ]
Kim, Yang-Shin [1 ]
Oh, Se-Young [1 ]
Lee, Jeihun [2 ]
机构：
[1] Pohang Univ Sci & TECHnol POSTECH, Dept Elect Engn, Pohang, South Korea
[2] LG Elect Inc, IT Lab, Seoul, South Korea
来源：
2012 IEEE INTELLIGENT VEHICLES SYMPOSIUM (IV) | 2012年
关键词：
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
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.
引用
收藏
页码：1029 / 1034
页数：6
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