In order to enable an autonomous mobile robot to travel over rough terrain, it necessitates the capability to detect self‐position accurately even when the odometry errors are increased in traveling. The conventional method can keep high speed and precise localization using iterative closest point algorithms or feature matching techniques. However, effects of steep changes of a mobile robot position are not considered when it travels over rough terrain. In this article, we propose the method for efficient real‐time 6D pose tracking using a rotating 2D laser scanner in traveling over rough terrain. For adaptation to steep changes of the position, weighted point clouds are generated based on the angular and the linear velocity measured by sensors mounted on the robot. And the position and posture of the robot are sequentially optimized by the scan matching in increments of 10 scans. In indoor experiments, we evaluated accuracy of our method when the robot passes on rugged floor. As a result, our method was performed with less than 0.078 m RMS positional error in real time.

中文翻译：

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3D扫描匹配，可在崎terrain的地形上定位移动机器人

为了使自主移动机器人能够在崎terrain的地形上行驶，即使在行驶中增加里程计误差的情况下，也必须能够准确检测自身位置。传统方法可以使用迭代最近点算法或特征匹配技术来保持高速和精确的定位。但是，当移动机器人在崎terrain的地形上行驶时，不会考虑其急剧变化的影响。在本文中，我们提出了一种用于在崎terrain不平的地面上行驶时使用旋转2D激光扫描仪进行高效实时6D姿态跟踪的方法。为了适应位置的急剧变化，将根据安装在机器人上的传感器测得的角速度和线速度生成加权点云。然后，通过以10次扫描为增量进行扫描匹配，依次优化机器人的位置和姿势。在室内实验中，我们评估了机器人在崎floor不平的地面上经过时我们方法的准确性。结果，我们的方法实时地以小于0.078 m RMS的位置误差执行。