This paper introduces a lightweight uncertainty estimator capable of predicting multimodal (disjoint) uncertainty bounds by integrating conformal prediction with a deep-learning regressor. We specifically discuss its application for visual odometry (VO), where environmental features such as flying domain symmetries and sensor measurements under ambiguities and occlusion can result in multimodal uncertainties. Our simulation results show that uncertainty estimates in our framework adapt sample-wise against challenging operating conditions such as pronounced noise, limited training data, and limited parametric size of the prediction model. We also develop a reasoning framework that leverages these robust uncertainty estimates and incorporates optical flow-based reasoning to improve prediction prediction accuracy. Thus, by appropriately accounting for predictive uncertainties of data-driven learning and closing their estimation loop via rule-based reasoning, our methodology consistently surpasses conventional deep learning approaches on all these challenging scenarios--pronounced noise, limited training data, and limited model size-reducing the prediction error by 2-3x.

翻译：本文提出了一种轻量级不确定性估计器，通过整合共形预测与深度学习回归器，能够预测多模态（非重叠）不确定性边界。我们具体讨论了其在视觉里程计中的应用，其中诸如飞行域对称性等环境特征以及存在歧义和遮挡下的传感器测量可能导致多模态不确定性。仿真结果表明，我们的框架中的不确定性估计能针对显著噪声、有限训练数据和预测模型参数规模受限等挑战性运行条件进行样本级自适应调整。我们还开发了一个推理框架，利用这些稳健的不确定性估计，并融合基于光流的推理来提高预测精度。因此，通过恰当考虑数据驱动学习的预测不确定性，并借助基于规则的推理闭合其估计环路，我们的方法在所有上述挑战性场景——显著噪声、有限训练数据和有限模型规模——中均持续超越传统深度学习方法，将预测误差降低2-3倍。