AK: Attentive Kernel for Information Gathering
AK: Attentive Kernel for Information Gathering
复制标题
DOI：
10.48550/arxiv.2205.06426
复制
发表时间：
2022-05
期刊：
影响因子：
--
通讯作者：
Weizhe (Wesley) Chen;R. Khardon;Lantao Liu
中科院分区：
文献类型：
--
作者：
Weizhe (Wesley) Chen;R. Khardon;Lantao Liu
机器人信息收集（RIG）依赖于概率模型的不确定性来识别关键区域以进行有效的数据收集。具有固定核的高斯过程（GPs）已被广泛用于空间建模。然而，真实世界的空间数据通常不满足平稳性的假设，其中不同的位置被假设为具有相同程度的可变性。因此，预测不确定性不能准确地捕获预测误差，限制了RIG算法的成功。我们提出了一个新的家庭的非平稳内核，命名为注意内核（AK），这是简单的，强大的，并可以扩展任何现有的内核到一个非平稳的。我们评估新的内核在海拔映射任务，AK提供更好的准确性和不确定性量化常用的RBF内核和其他流行的非平稳内核。改进的不确定性量化指导下游RIG计划人员在高误差区域周围收集更多有价值的数据，进一步提高预测精度。现场实验表明，所提出的方法可以指导自主地面车辆（ASV）优先数据收集的位置与高空间变化，使模型的特点，突出的环境特征。
Robotic Information Gathering (RIG) relies on the uncertainty of a probabilistic model to identify critical areas for efficient data collection. Gaussian processes (GPs) with stationary kernels have been widely adopted for spatial modeling. However, real-world spatial data typically does not satisfy the assumption of stationarity, where different locations are assumed to have the same degree of variability. As a result, the prediction uncertainty does not accurately capture prediction error, limiting the success of RIG algorithms. We propose a novel family of nonstationary kernels, named the Attentive Kernel (AK), which is simple, robust, and can extend any existing kernel to a nonstationary one. We evaluate the new kernel in elevation mapping tasks, where AK provides better accuracy and uncertainty quantification over the commonly used RBF kernel and other popular nonstationary kernels. The improved uncertainty quantification guides the downstream RIG planner to collect more valuable data around the high-error area, further increasing prediction accuracy. A field experiment demonstrates that the proposed method can guide an Autonomous Surface Vehicle (ASV) to prioritize data collection in locations with high spatial variations, enabling the model to characterize the salient environmental features.