Deep learning offers promising new ways to accurately model aleatoric
uncertainty in robotic 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.

深度学习方法在建模机器人估计系统中的不确定性上表现出了巨大潜力，特别是当不确定性分布不符合传统的固定和高斯假设时。本研究通过三种基本的深度学习方法（参数化模型、离散化模型和生成模型）对非高斯型不确定性进行了数值建模和评估，并在模拟非高斯密度和现实世界的地形相对导航数据上系统比较了这三种方法的优缺点。结果表明，这些深度学习方法能够准确捕捉复杂的不确定性模式，彰显了它们提高估计系统可靠性和鲁棒性的潜力。