Robot pose estimation in unknown environments by matching 2D range scans
Robot pose estimation in unknown environments by matching 2D range scans
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DOI：
10.1023/a:1007957421070
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发表时间：
1997-03-01
影响因子：
3.3
通讯作者：
Milios, E
中科院分区：
文献类型：
--
作者：
Lu, F;Milios, E
探索未知环境的移动的机器人除了通过其传感器检测到的特征之外，没有绝对的位置参考系。使用可区分的地标是一种可能的方法，但它需要解决对象识别问题。特别是，当机器人使用二维激光距离扫描进行定位时，很难从距离扫描中准确检测和定位环境中的地标（例如角落和遮挡）。本文中，我们开发了两种新的迭代算法来将距离扫描配准到先前的扫描，以便计算未知环境中的相对机器人位置，从而避免上述问题。第一种算法是基于两次扫描中的切线方向的匹配数据点，并最小化距离函数，以解决扫描之间的位移。第二种算法建立两次扫描中的点之间的对应关系，然后解决点对点最小二乘问题以计算两次扫描的相对姿态。我们的方法在弯曲的环境中工作，可以通过拒绝离群值来处理部分遮挡。
A mobile robot exploring an unknown environment has no absolute frame of reference for its position, other than features it detects through its sensors. Using distinguishable landmarks is one possible approach, but it requires solving the object recognition problem. In particular, when the robot uses two-dimensional laser range scans for localization, it is difficult to accurately detect and localize landmarks in the environment (such as corners and occlusions) from the range scans.In this paper, we develop two new iterative algorithms to register a range scan to a previous scan so as to compute relative robot positions in an unknown environment, that avoid the above problems. The first algorithm is based on matching data points with tangent directions in two scans and minimizing a distance function in order to solve the displacement between the scans. The second algorithm establishes correspondences between points in the two scans and then solves the point-to-point least-squares problem to compute the relative pose of the two scans. Our methods work in curved environments and can handle partial occlusions by rejecting outliers.