  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.
