  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.

Robotics