Perceptual Factors for Environmental Modeling in Robotic Active Perception (2309.10620v2)

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.

• The paper introduces a model that factors in environmental correlations to enhance sensor measurements in active robotics.
• The paper integrates its perceptual map with recursive estimation methods like the Extended Kalman Filter to reduce estimation errors.
• The paper demonstrates its framework’s effectiveness through simulated and real-world experiments, achieving improved classification accuracy.

Overview of Perceptual Factors for Environmental Modeling in Robotic Active Perception

This paper introduces a novel framework for active perception in robotics, focusing on the environmental modeling of perceptual factors that impact high-level sensor measurements. The core objective is to improve the information gain when planning viewpoints for robots by modeling the correlation of measurements with environmental factors such as occluders, lighting conditions, and the redundancy of data arising from similar viewpoints. The proposed framework is evaluated in both simulated and real-world environments, demonstrating its efficacy in facilitating robust perception and decision-making in robotics.

Key Contributions

The paper makes several key contributions to the domain of robotic perception and planning:

1. Perception Model with Environmental Correlations: The proposed model enhances traditional perception models by incorporating a set of perceptual factors, which are mathematical functions designed to capture the influence of the environment on sensor measurements. These perceptual factors are aggregated into a perceptual map that is used in planning and sensor fusion processes.
2. Integration with Recursive Estimation Frameworks: The authors integrate their perceptual map into recursive estimation processes, namely the Extended Kalman Filter (EKF) for continuous variables and a categorical fusion model for semantic classification tasks. This integration allows for an adjustment in measurement uncertainty, thereby improving the consistency and robustness of sensor data utilization.
3. Simulated and Real-World Evaluation: The framework is assessed through object pose estimation and semantic classification tasks in simulated environments introduced with occlusion and lighting challenges. Additionally, a real-world scenario using a ground robot for active object classification tasks showcases the applicability and benefits of the proposed model.

Numerical Results and Claims

The experimental results indicate significant improvements in state estimation and classification tasks when accounting for environmental effects through perceptual factors. Specifically, the authors report reductions in the Normalized Estimated Error Squared (NEES) and Root Mean Squared Error (RMSE) for state estimation tasks. In semantic classification, introducing perceptual factors results in higher classification accuracy and confidence in predictions.

Implications and Future Directions

The implications of integrating perceptual factors into robotic active perception are notable. The approach enhances the ability of robots to make informed decisions based on visual data in dynamic and uncertain environments. This advancement potentially leads to more efficient autonomous systems capable of complex tasks such as navigation, exploration, and detailed scene understanding.

From a theoretical standpoint, the modeling of perceptual factors raises intriguing possibilities for extending these ideas through machine learning techniques to capture more nuanced environmental effects. Practically, the approach suggests improvements in the development of autonomous systems where environmental variability is a significant challenge.

Future research could explore various avenues:

• Expanding the framework to include additional sensory modalities, such as auditory or tactile sensors.
• Leveraging advances in machine learning to automatically learn perceptual factors from large datasets.
• Investigating the scalability of the proposed model in more complex environments and tasks.

In conclusion, this paper contributes a sophisticated methodology for enhancing robotic perception by embracing the complex interplay between measurements and environmental effects, offering promising pathways for both academic inquiry and practical robotics applications.