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.

翻译：深度学习为机器人估计系统中偶然不确定性的精确建模提供了前景广阔的新方法，尤其适用于不确定性分布不满足传统固定高斯假设的情形。本研究针对非高斯偶然不确定性的量化问题，构建并评估了三种用于条件概率密度建模的基础深度学习方法：参数化建模、离散化建模与生成式建模。我们在模拟的非高斯密度分布以及真实世界地形相对导航数据上，系统比较了这三种方法各自的优势与局限。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，彰显了其在提升估计系统可靠性与鲁棒性方面的潜力。