Accurately assessing the potential value of new sensor observations is a critical aspect of planning for active perception. This task is particularly challenging when reasoning about high-level scene understanding using measurements from vision-based neural networks. Due to appearance-based reasoning, the measurements are susceptible to several environmental effects such as the presence of occluders, variations in lighting conditions, and redundancy of information due to similarity in appearance between nearby viewpoints. To address this, we propose a new active perception framework incorporating an arbitrary number of perceptual effects in planning and fusion. Our method models the correlation with the environment by a set of general functions termed perceptual factors to construct a perceptual map, which quantifies the aggregated influence of the environment on candidate viewpoints. This information is seamlessly incorporated into the planning and fusion processes by adjusting the uncertainty associated with measurements to weigh their contributions. We evaluate our perceptual maps in a simulated environment that reproduces environmental conditions common in robotics applications. Our results show that, by accounting for environmental effects within our perceptual maps, we improve in the state estimation by correctly selecting the viewpoints and considering the measurement noise correctly when affected by environmental factors. We furthermore deploy our approach on a ground robot to showcase its applicability for real-world active perception missions.

翻译：准确评估新传感器观测的潜在价值是规划主动感知的关键环节。当利用基于视觉的神经网络测量进行高层场景理解推理时，这一任务尤为具有挑战性。由于基于外观的推理机制，测量结果易受多种环境效应影响，例如遮挡物存在、光照条件变化以及相邻视角因外观相似导致的信息冗余。为此，我们提出了一种新的主动感知框架，能够在规划与融合过程中纳入任意数量的感知效应。该方法通过一组称为感知因素的通用函数对环境相关性进行建模，构建可量化环境对候选视角聚合影响的感知图。通过调整与测量相关的不确定性以权衡其贡献，该信息被无缝整合到规划与融合过程中。我们在模拟环境中评估了感知图，该环境复现了机器人应用中常见的环境条件。结果表明：通过将环境效应纳入感知图，我们能够正确选择视角，并在受环境因素影响时恰当考虑测量噪声，从而提升状态估计精度。此外，我们还将该方法部署于地面机器人，以展示其在实际主动感知任务中的适用性。