This letter fosters the idea that deep learning methods can be used to complement classical visual odometry pipelines to improve their accuracy and to associate uncertainty models to their estimations. We show that the biases inherent to the visual odometry process can be faithfully learned and compensated for, and that a learning architecture associated with a probabilistic loss function can jointly estimate a full covariance matrix of the residual errors, defining an error model capturing the heteroscedasticity of the process. Experiments on autonomous driving image sequences assess the possibility to concurrently improve visual odometry and estimate an error associated with its outputs.

中文翻译：

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同时学习基于几何的视觉里程计方法的修正和误差模型

这封信提出了这样一种想法，即深度学习方法可用于补充经典的视觉里程计管道，以提高其准确性并将不确定性模型与其估计相关联。我们表明视觉里程计过程固有的偏差可以被忠实地学习和补偿，并且与概率损失函数相关的学习架构可以联合估计残差的完整协方差矩阵，定义一个误差模型来捕捉过程。自动驾驶图像序列的实验评估了同时改进视觉里程计和估计与其输出相关的误差的可能性。