Over the past few years, we have witnessed a considerable diffusion of data-driven visual odometry (VO) approaches as viable alternatives to standard geometric-based strategies. Their success is mainly related to the improved robustness to image nonideal conditions (e.g., blur, high or low contrast, texture-poor scenarios). However, most of the data-driven State-of-the-Art (SotA) approaches do not provide any kind of information about the uncertainty of their estimates, which is crucial to effectively integrate them into robotic navigation systems. Inspired by this considerations, we propose uncertainty-aware VO (UA-VO), a novel deep neural network (DNN) architecture that computes relative pose predictions by processing sequence of images and, at the same time, provides uncertainty measures about those estimations. The confidence measure computed by UA-VO considers both epistemic and aleatoric uncertainties and accounts for heteroscedasticity, i.e., it is sample-dependent. We assess the benefits of UA-VO with different typology of experiments on three publicly available datasets and on a brand new set of sequences, we gathered to extend the evaluation.

中文翻译：

---

数据驱动视觉里程计的不确定性估计

在过去的几年里，我们目睹了数据驱动的视觉里程计 (VO) 方法作为标准几何策略的可行替代方案的大量传播。他们的成功主要与提高对图像非理想条件（例如，模糊、高或低对比度、纹理较差的场景）的鲁棒性有关。然而，大多数数据驱动的最新技术 (SotA) 方法不提供有关其估计不确定性的任何类型的信息，这对于将它们有效地集成到机器人导航系统中至关重要。受此考虑的启发，我们提出了不确定性感知 VO (UA-VO)，这是一种新颖的深度神经网络 (DNN) 架构，它通过处理图像序列来计算相对姿态预测，同时提供有关这些估计的不确定性度量。UA-VO 计算的置信度既考虑了认知不确定性，也考虑了任意不确定性，并考虑了异方差性，即它依赖于样本。我们在三个公开可用的数据集和一组全新的序列上使用不同类型的实验评估 UA-VO 的好处，我们收集以扩展评估。