Robotic Information Gathering (RIG) is a foundational research topic that answers how a robot (team) collects informative data to efficiently build an accurate model of an unknown target function under robot embodiment constraints. RIG has many applications, including but not limited to autonomous exploration and mapping, 3D reconstruction or inspection, search and rescue, and environmental monitoring. A RIG system relies on a probabilistic model's prediction uncertainty to identify critical areas for informative data collection. Gaussian Processes (GPs) with stationary kernels have been widely adopted for spatial modeling. However, real-world spatial data is typically non-stationary -- different locations do not have the same degree of variability. As a result, the prediction uncertainty does not accurately reveal prediction error, limiting the success of RIG algorithms. We propose a family of non-stationary kernels named Attentive Kernel (AK), which is simple, robust, and can extend any existing kernel to a non-stationary one. We evaluate the new kernel in elevation mapping tasks, where AK provides better accuracy and uncertainty quantification over the commonly used stationary kernels and the leading non-stationary kernels. The improved uncertainty quantification guides the downstream informative 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 significant spatial variations, enabling the model to characterize salient environmental features.

翻译：机器人信息获取（RIG）是一个基础研究课题，旨在解答机器人在自身具身约束下如何（以团队形式）收集有价值数据，以高效构建未知目标函数的精确模型。RIG具有广泛的应用，包括但不限于自主探索与建图、三维重建或检测、搜救以及环境监测。RIG系统依赖于概率模型的预测不确定性来识别关键区域，以进行有价值的数据收集。采用平稳核函数的高斯过程（GPs）已被广泛应用于空间建模。然而，真实世界的空间数据通常是非平稳的——不同位置不具有相同的变异性。因此，预测不确定性无法准确反映预测误差，限制了RIG算法的成功。我们提出了一类名为注意力核（AK）的非平稳核函数，它简单、鲁棒，并能将任何现有核函数扩展为非平稳形式。我们在地形建图任务中评估了新核函数，结果表明AK在精度和不确定性量化方面均优于常用的平稳核函数及领先的非平稳核函数。改进后的不确定性量化引导下游信息型规划器在高误差区域周围收集更有价值的数据，从而进一步提高预测精度。一项现场实验表明，所提方法能够引导自主水面航行器（ASV）优先在空间变化显著的位置收集数据，使模型能够表征关键的环境特征。