Active perception approaches select future viewpoints by using some estimate of the information gain. An inaccurate estimate can be detrimental in critical situations, e.g., locating a person in distress. However the true information gained can only be calculated post hoc, i.e., after the observation is realized. We present an approach for estimating the discrepancy between the information gain (which is the average over putative future observations) and the true information gain. The key idea is to analyze the mathematical relationship between active perception and the estimation error of the information gain in a game-theoretic setting. Using this, we develop an online estimation approach that achieves sub-linear regret (in the number of time-steps) for the estimation of the true information gain and reduces the sub-optimality of active perception systems. We demonstrate our approach for active perception using a comprehensive set of experiments on: (a) different types of environments, including a quadrotor in a photorealistic simulation, real-world robotic data, and real-world experiments with ground robots exploring indoor and outdoor scenes; (b) different types of robotic perception data; and (c) different map representations. On average, our approach reduces information gain estimation errors by 42%, increases the information gain by 7%, PSNR by 5%, and semantic accuracy (measured as the number of objects that are localized correctly) by 6%. In real-world experiments with a Jackal ground robot, our approach demonstrated complex trajectories to explore occluded regions.

翻译：主动感知方法通过利用信息增益的某种估计来选择未来的视点。不准确的估计在关键情况下可能是有害的，例如，定位处于困境中的人员。然而，真实获得的信息只能在事后计算，即在观测实现之后。我们提出了一种方法来估计信息增益（即对未来潜在观测的平均）与真实信息增益之间的差异。其核心思想是在博弈论框架下分析主动感知与信息增益估计误差之间的数学关系。基于此，我们开发了一种在线估计方法，该方法在真实信息增益的估计上实现了次线性遗憾（关于时间步数），并降低了主动感知系统的次优性。我们通过一系列综合实验验证了所提出的主动感知方法：（a）不同类型的环境，包括在照片级真实感模拟中的四旋翼飞行器、真实世界机器人数据，以及地面机器人在室内外场景中进行探索的真实世界实验；（b）不同类型的机器人感知数据；以及（c）不同的地图表示。平均而言，我们的方法将信息增益估计误差降低了42%，将信息增益提高了7%，峰值信噪比（PSNR）提高了5%，语义准确度（以正确定位的物体数量衡量）提高了6%。在使用Jackal地面机器人进行的真实世界实验中，我们的方法展示了为探索被遮挡区域而生成的复杂轨迹。