Autonomous Exploration with Expectation-Maximization
Autonomous Exploration with Expectation-Maximization
复制标题
期望最大化的自主探索
DOI：
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发表时间：
2017
期刊：
影响因子：
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通讯作者：
Brendan Englot
中科院分区：
文献类型：
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作者：
Jinkun Wang;Brendan Englot
研究了移动机器人在未知环境下的自主探索问题，目的是高效地建立精确的基于特征的地图。关于该主题的大多数文献都集中在多种效用函数的组合上，例如抑制机器人姿态不确定性和占用网格地图的熵。然而，不确定姿势的影响通常不能很好地纳入惩罚定位不良，这最终会导致不准确的地图。相反，我们明确地将未知地标建模为潜在变量，并预测其预期的不确定性，将其纳入与基于采样的运动规划一起使用的效用函数，以产生信息丰富且低不确定性的运动原语。我们提出了一种迭代期望最大化算法来执行规划过程，驱动机器人逐步探索未知环境。在模拟实验中，我们对算法的性能进行了分析，结果表明，我们的算法在保持与竞争算法相同的搜索覆盖速度的同时，有效地提高了生成地图的质量。
We consider the problem of autonomous mobile robot exploration in an unknown environment for the purpose of building an accurate feature-based map efficiently. Most literature on this subject is focused on the combination of a variety of utility functions, such as curbing robot pose uncertainty and the entropy of occupancy grid maps. However, the effect of uncertain poses is typically not well incorporated to penalize poor localization, which ultimately leads to an inaccurate map. Instead, we explicitly model unknown landmarks as latent variables, and predict their expected uncertainty, incorporating this into a utility function that is used together with sampling-based motion planning to produce informative and low-uncertainty motion primitives. We propose an iterative expectation-maximization algorithm to perform the planning process driving a robot’s step-by-step exploration of an unknown environment. We analyze the performance in simulated experiments, showing that our algorithm maintains the same coverage speed in exploration as competing algorithms, but effectively improves the quality of the resulting map.