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fast-search-slam's Introduction

本人研究生关于激光SLAM的一部分工作(激光SLAM的论文有人收真的不容易,感谢机器人期刊),有条件的欢迎下载,希望有感兴趣的同学可以一起完善。

论文下载链接:基于多分辨率搜索与多点云密度匹配的快速ICP-SLAM方法 https://kns.cnki.net/KCMS/detail/21.1137.TP.20200515.1456.001.html?v=MDYwNzBybFVyckpKRjQ9THp6WmZMRzRITkhNcW81QVpPc1BZdzlNem1SbjZqNTdUM2ZscVdNMENMTDdSN3FkWitab0Zp

摘要:针对激光SLAM (同步定位与地图创建)的实时性和定位精度问题,为了克服初始位姿不准确情况下增大搜索范围和位姿匹配分辨率对实时性的影响,本文在传统ICP-SLAM (迭代最近邻SLAM)基础上进行改进,提出了一种分层搜索与匹配的快速ICP-SLAM方法.首先,在搜索范围内采用由粗到细的分辨率进行全局搜索,并通过逐渐增加待匹配点的密度进行分步匹配计算.点云匹配过程中,通过构建距离像计算待匹配点的最近邻距离值,其计算复杂度降低为O(1).其次,通过对点云匹配结果进行优先排序和剪枝,快速排除非最优解.最后,以半数全局最优与全数局部最优原则作为搜索结束判断条件,提高搜索效率.SLAM Benchmark数据集上的测试结果表明,相比于流行的激光SLAM算法Cartographer,所提出的方法取得了更小的平均误差和平方误差,计算效率为Cartographer算法的2~5倍.同时,工业AGV (自动导引车)的实际应用实验验证了在初始位姿未知的情况下,可实现实时的位姿估计与建图,重复定位精度优于1.5 cm.因此,这种快速ICP-SLAM方法能够保证实现准确的定位估计,具有良好的实时性. 

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