一种有效的移动目标混合扫描匹配算法

K. Lenac, A. Cuzzocrea, E. Mumolo
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引用次数: 1

摘要

本文分析了混合扫描匹配算法,并在典型的移动应用中测试了它们的性能。由于遗传算法是鲁棒的,但不是很精确,ICP是准确的,但不是很鲁棒,所以很自然地以级联方式使用这两种算法:首先我们运行遗传优化来找到一个近似但鲁棒的匹配解,然后我们运行迭代最近点(ICP)算法来提高精度。由于采用了查找表的形式,该算法的速度非常快,并且对于扫描数据采集过程中距离和角度的较大误差具有很强的鲁棒性。值得一提的是,在移动对象应用程序中,由于车轮滑动或闭合循环,通常会出现大的扫描错误。实验结果表明,该算法能够成功地处理较大的定位误差。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An effective and efficient hybrid scan matching algorithm for mobile object applications
In this paper we analyze hybrid scan matching algorithms and we test their performances in typical mobile applications. Since the genetic algorithm is robust but not very accurate, and ICP is accurate but not very robust, it is natural to use the two algorithms in a cascade fashion: first we run a genetic optimization to find an approximate but robust matching solution and then we run the Iterative Closest Point (ICP) algorithm to increase the accuracy. The proposed genetic algorithm is very fast due to a look-up table formulation and very robust against large errors in both distance and angle during scan data acquisition. It is worth mentioning that large scan errors arise very commonly in mobile object applications due, for instance, to wheel slippage or when closing loops. We show experimentally that the proposed algorithm successfully copes with large localization errors.
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