Critical Rays Scan Match SLAM
Critical Rays Scan Match SLAM
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DOI：
10.1007/s10846-012-9811-5
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发表时间：
2013-02
影响因子：
3.3
通讯作者：
E. Tsardoulias;L. Petrou
中科院分区：
文献类型：
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作者：
E. Tsardoulias;L. Petrou
扫描匹配是用于基于占用网格的SLAM的最古老且最简单的方法之一。其总体思路是通过计算激光扫描与其前身之间的2-D变换来找到机器人的姿态并更新其地图。由于其简单性，许多解决方案被提出并用于各种系统中，其中绝大多数是迭代的。事实上，尽管扫描匹配在其实现中是简单的，但是它遭受累积噪声。当然，在结果的质量和所需的执行时间之间肯定存在权衡。为了在较小的迭代时间内获得高质量的映射，已经引入了许多算法，从而可以实现在线执行。所提出的SLAM方案通过实施射线选择方法来执行扫描匹配。其主要思想是通过预处理扫描和选择对匹配过程至关重要的射线来降低匹配所需的复杂性和时间。本文比较了几种不同的光线选择方法。此外，在当前扫描和全局机器人地图之间执行匹配，以便最小化累积误差。RRHC（Random Restart Hill Climbing）用于将扫描匹配到地图，这是一种局部搜索优化过程，可以很容易地参数化，并且比传统的遗传算法（GA）快得多，这主要是因为问题的复杂度低。总体思路是构建一个可参数化的SLAM，可用于需要低计算成本的在线系统。该算法假设一个结构化的民用环境，面向使用在RoboCup-RoboRescue比赛，其主要目的是构建高质量的地图。
Scan matching is one of the oldest and simplest methods for occupancy grid based SLAM. The general idea is to find the pose of a robot and update its map simply by calculating the 2-D transformation between a laser scan and its predecessor. Due to its simplicity many solutions were proposed and used in various systems, the vast majority of which are iterative. The fact is, that although scan matching is simple in its implementation, it suffers from accumulative noise. Of course, there is certainly a trade-off between the quality of results and the execution time required. Many algorithms have been introduced, in order to achieve good quality maps in a small iteration time, so that on-line execution would be achievable. The proposed SLAM scheme performs scan matching by implementing a ray-selection method. The main idea is to reduce complexity and time needed for matching by pre-processing the scan and selecting rays that are critical for the matching process. In this paper, several different methods of ray-selection are compared. In addition matching is performed between the current scan and the global robot map, in order to minimize the accumulated errors. RRHC (Random Restart Hill Climbing) is employed for matching the scan to the map, which is a local search optimization procedure that can be easily parameterized and is much faster than a traditional genetic algorithm (GA), largely because of the low complexity of the problem. The general idea is to construct a parameterizable SLAM that can be used in an on-line system that requires low computational cost. The proposed algorithm assumes a structured civil environment, is oriented for use in the RoboCup - RoboRescue competition, and its main purpose is to construct high quality maps.