Deep learning has the potential to dramatically impact navigation and
tracking state estimation problems critical to autonomous vehicles and
robotics. Measurement uncertainties in state estimation systems based on Kalman
and other Bayes filters are typically assumed to be a fixed covariance matrix.
This assumption is risky, particularly for "black box" deep learning models, in
which uncertainty can vary dramatically and unexpectedly. Accurate
quantification of multivariate uncertainty will allow for the full potential of
deep learning to be used more safely and reliably in these applications. We
show how to model multivariate uncertainty for regression problems with neural
networks, incorporating both aleatoric and epistemic sources of heteroscedastic
uncertainty. We train a deep uncertainty covariance matrix model in two ways:
directly using a multivariate Gaussian density loss function, and indirectly
using end-to-end training through a Kalman filter. We experimentally show in a
visual tracking problem the large impact that accurate multivariate uncertainty
quantification can have on Kalman filter performance for both in-domain and
out-of-domain evaluation data. We additionally show in a challenging visual
odometry problem how end-to-end filter training can allow uncertainty
predictions to compensate for filter weaknesses.

本文论述了深度学习在自主驾驶和机器人中导航和状态估计中的应用，为了在黑盒模型和卡尔曼和其他贝叶斯滤波器中更加稳妥可靠地使用深度学习，需要准确地量化多变量不确定性，提出并实验了通过神经网络建模多元不确定性的方法，并证明精确的多变量不确定性量化对于卡尔曼滤波器性能在领域内和领域外评估数据中的巨大影响，同时指出端到端滤波器训练可以允许不确定性预测来补偿滤波器的不足。