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：机器人信息采集（Robotic Information Gathering，RIG）是一个基础性研究课题，旨在解决机器人（集群）如何在自身物理约束下收集信息丰富的数据，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地图构建、三维重建与检测、搜索救援及环境监测。RIG 系统依赖概率模型的预测不确定性来识别信息丰富的关键区域进行数据采集。采用平稳核函数的高斯过程（Gaussian Processes，GPs）已被广泛应用于空间建模。然而，现实世界中的空间数据通常是非平稳的——不同位置具有不同的变异程度。因此，预测不确定性无法准确反映预测误差，从而限制了 RIG 算法的有效性。我们提出一类名为注意力核（Attentive Kernel，AK）的非平稳核函数，它简单、稳健，并能将任何现有核函数扩展为非平稳形式。我们在高程映射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主流非平稳核函数。改进的不确定性量化引导下游信息导向规划器在高误差区域收集更有价值的数据，进一步提升预测精度。现场实验表明，所提方法能够引导自主水面航行器（Autonomous Surface Vehicle，ASV）优先在空间变异显著的区域采集数据，从而使模型有效刻画关键环境特征。