Accurate state and uncertainty estimation is imperative for mobile robots and self driving vehicles to achieve safe navigation in pedestrian rich environments. A critical component of state and uncertainty estimation for robot navigation is to perform robustly under out-of-distribution noise. Traditional methods of state estimation decouple perception and state estimation making it difficult to operate on noisy, high dimensional data. Here, we describe an approach that combines the expressiveness of deep neural networks with principled approaches to uncertainty estimation found in recursive filters. We particularly focus on techniques that provide better robustness to out-of-distribution noise and demonstrate applicability of our approach on two scenarios: a simple noisy pendulum state estimation problem and real world pedestrian localization using the nuScenes dataset. We show that our approach improves state and uncertainty estimation compared to baselines while achieving approximately 3x improvement in computational efficiency.

翻译：准确的状态和不确定性估算对于移动机器人和自我驾驶工具在行人丰富环境中实现安全航行至关重要。对于机器人导航来说,状态和不确定性估算的一个关键组成部分是,在分配外的噪音下进行稳健的状态和不确定性估算。传统的状态估算方法是分流感和状态估算,使得难以在吵闹的高维数据上运行。这里,我们描述了一种方法,将深神经网络的清晰度与循环过滤器中发现的不确定性估算的原则性方法结合起来。我们特别侧重于那些为超出分配的噪音提供更好稳健性的技术,并展示了我们在两种情景上采用的方法:简单的杂乱的钟状状态估算问题,以及使用Nuscenes数据集实际的世界行人本地化。我们表明,我们的方法比基线改进了状态和不确定性估算,同时在计算效率方面实现了大约3x的改进。