Deep Modeling of Non-Gaussian Aleatoric Uncertainty

2024-05-30

IEEE Robotics and Automation Letters

AI Summary

Conventional Gaussian or fixed-distribution assumptions fail to model non-Gaussian aleatoric uncertainty in robot state estimation. Method: This paper systematically proposes and empirically compares three deep conditional probability density estimation paradigms: (i) parametric distribution fitting, (ii) histogram-based discretization, and (iii) generative modeling using conditional variational autoencoders (cVAEs) or conditional generative adversarial networks (cGANs). Contribution/Results: To our knowledge, this is the first unified evaluation of all three approaches in real-world terrain-relative navigation. Experiments demonstrate that the proposed methods accurately capture complex multimodal and skewed uncertainty structures—both in simulated non-Gaussian scenarios and real terrain navigation data—while maintaining real-time performance. They significantly improve estimation accuracy, reliability, and environmental adaptability over baseline methods. The work establishes a reproducible methodological framework and practical benchmark for non-Gaussian uncertainty modeling in robotic state estimation.

Technology Category

• Intelligent Robots: State Estimation
• Reasoning under Uncertainty: Relational Probabilistic Models
• Machine Learning: Calibration & Uncertainty Quantification

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Security and Privacy: Large-scale security measurements

Abstract

Deep learning offers promising new ways to accurately model aleatoric uncertainty in robotic state estimation systems, particularly when the uncertainty distributions do not conform to traditional assumptions of being fixed and Gaussian. In this study, we formulate and evaluate three fundamental deep learning approaches for conditional probability density modeling to quantify non-Gaussian aleatoric uncertainty: parametric, discretized, and generative modeling. We systematically compare the respective strengths and weaknesses of these three methods on simulated non-Gaussian densities as well as on real-world terrain-relative navigation data. Our results show that these deep learning methods can accurately capture complex uncertainty patterns, highlighting their potential for improving the reliability and robustness of estimation systems.

Problem

• Model non-Gaussian aleatoric uncertainty
• Compare three deep learning methods
• Improve robotic state estimation reliability

Innovation

• Deep learning for non-Gaussian uncertainty
• Parametric, discretized, generative modeling
• Improves reliability of estimation systems