Informative Planning in the Presence of Outliers
Informative Planning in the Presence of Outliers
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DOI：
10.1109/icra46639.2022.9812267
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发表时间：
2021-11
期刊：
影响因子：
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通讯作者：
Weizhe (Wesley) Chen;Lantao Liu
中科院分区：
文献类型：
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作者：
Weizhe (Wesley) Chen;Lantao Liu
信息规划寻求一系列行动，引导机器人收集最具信息性的数据，以建立大规模的环境模型或学习动态系统。现有的信息规划工作主要集中在提出新的规划者，并将其应用于各种机器人应用，如环境监测，自主探索和系统识别。信息规划器优化由概率模型给出的目标，例如，高斯过程回归（GPR）。在实际应用中，普遍存在的感知异常值很容易影响模型，导致误导目标。一个简单的解决方案是使用现成的离群值检测器过滤掉感测数据流中的离群值。然而，根据定义，信息样本也是稀缺的，因此它们可能会被错误地过滤掉。在本文中，我们提出了一种方法，使机器人重新访问的位置，离群值采样，除了优化信息规划目标。机器人可以在异常值附近收集更多的样本，并更新异常值检测器以减少误报的数量。我们通过设计一个新的Pareto Monte Carlo树搜索（MCTS）的目标来实现这一点。我们证明了所提出的框架比天真地应用离群值检测器执行得更好。
Informative planning seeks a sequence of actions that guide the robot to collect the most informative data to build a large-scale environmental model or learn a dynamical system. Existing work in informative planning mainly focuses on proposing new planners and applying them to various robotic applications such as environmental monitoring, autonomous exploration, and system identification. The informative planners optimize an objective given by a probabilistic model, e.g., Gaussian process regression (GPR). In practice, the ubiquitous sensing outliers can easily affect the model, resulting in a misleading objective. A straightforward solution is to filter out the outliers in the sensing data stream using an off-the-shelf outlier detector. However, informative samples are also scarce by definition so they might be falsely filtered out. In this paper, we propose a method to enable the robot to re-visit the locations where outliers were sampled besides optimizing the informative planning objective. The robot can collect more samples in the vicinity of outliers and update the outlier detector to reduce the number of false alarms. We achieve this by designing a new objective for the Pareto Monte Carlo tree search (MCTS). We demonstrate that the proposed framework performs better than applying an outlier detector naively.