Perceptual Factors for Environmental Modeling in Robotic Active Perception

TL;DR

This paper introduces a novel active perception framework that models environmental effects using perceptual factors to improve viewpoint selection and state estimation in robotic perception tasks.

Contribution

It proposes a new method that incorporates environmental effects into perceptual maps, enhancing planning and fusion in active perception systems.

Findings

Improved state estimation by accounting for environmental effects.

Effective viewpoint selection considering environmental influences.

Validated on both simulated and real-world robotic platforms.

Abstract

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.