A Weighted Range Sensor Matching Algorithm for Mobile Robot Displacement Estimation
A Weighted Range Sensor Matching Algorithm for Mobile Robot Displacement Estimation
复制标题
一种用于移动机器人位移估计的加权距离传感器匹配算法
DOI：
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发表时间：
2002
期刊：
影响因子：
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通讯作者：
J. Burdick
中科院分区：
文献类型：
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作者：
Samuel T. Pster;K. Kriechbaum;S. Roumeliotis;J. Burdick
本文介绍了一种(CID：147)加权(CID：148)匹配算法，通过匹配密集的二维距离扫描来估计机器人的平面位移。该算法基于预期传感器不确定性模型，根据各扫描点的不确定性对各扫描点对整体匹配误差的贡献度进行加权。一般的最大似然公式被用来最优地估计两个连续姿势之间的位移。我们开发了考虑测量噪声、传感器入射角和通信误差等影响的不确定度模型。通过对这些噪声源进行显式建模，我们可以计算出比以前的工作更真实的位移估计协方差。当在定位框架内进一步将位移估计与里程计和/或惯性测量相结合时，需要实际的协方差估计。使用Nomad 200移动机器人和患病的LMS-200激光靶场(CID：2)进行的实验表明，该方法比现有技术更准确。
This paper introduces a (cid:147)weighted(cid:148) matching algorithm to estimate a robot’s planar displacement by matching dense two-dimensional range scans. Based on models of expected sensor uncertainty, our algorithm weights the contribution of each scan point to the overall matching error according to its uncertainty. A general maximum likelihood formulation is used to optimally estimate the displacement between two consecutive poses. We develop uncertainty models that account for effects such as measurement noise, sensor incidence angle, and correspondence error. By explicitly modeling these noise sources, we can calculate a more realistic covariance of the displacement estimate than is done in prior work. A realistic covariance estimate is needed when further combining the displacement estimates with odometric and/or inertial measurements within a localization framework. Experiments using a Nomad 200 mobile robot and a Sick LMS-200 laser range (cid:2)nder illustrate that the method is more accurate than prior techniques.