Real-time correlative scan matching
Real-time correlative scan matching
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DOI：
10.1109/robot.2009.5152375
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发表时间：
2009-05
期刊：
影响因子：
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通讯作者：
Edwin Olson
中科院分区：
文献类型：
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作者：
Edwin Olson
扫描匹配是移动机器人最依赖的工具之一，它是为了确定获得扫描的相对位置而对两个激光扫描进行匹配的问题。目前的算法，在计算性能的权衡，采用启发式，以快速计算出一个答案。当然，这些启发式是不完美的：现有的方法可能会产生糟糕的结果，特别是当先验很弱的时候。现代机器人可用的计算能力保证了对这些质量与复杂性权衡的重新审视。在本文中，我们提倡一种概率驱动的扫描匹配算法，该算法以额外的计算时间为代价产生更高质量和更鲁棒的结果。我们描述了这种方法在现代硬件上实现实时性能的几种新实现，包括用于传统cpu的多分辨率方法和用于图形处理单元（gpu）的并行方法。我们还提供了我们的方法和几个当代方法的实证评估，说明了我们的方法的好处。这些方法的鲁棒性使它们对全局循环关闭特别有用。
Scan matching, the problem of registering two laser scans in order to determine the relative positions from which the scans were obtained, is one of the most heavily relied-upon tools for mobile robots. Current algorithms, in a trade-off for computational performance, employ heuristics in order to quickly compute an answer. Of course, these heuristics are imperfect: existing methods can produce poor results, particularly when the prior is weak. The computational power available to modern robots warrants a re-examination of these quality vs. complexity trade-offs. In this paper, we advocate a probabilistically-motivated scan-matching algorithm that produces higher quality and more robust results at the cost of additional computation time. We describe several novel implementations of this approach that achieve real-time performance on modern hardware, including a multi-resolution approach for conventional CPUs, and a parallel approach for graphics processing units (GPUs). We also provide an empirical evaluation of our methods and several contemporary methods, illustrating the benefits of our approach. The robustness of the methods make them especially useful for global loop-closing.