论文标题
同时学习基于几何学的视觉探光方法的校正和错误模型
Simultaneously Learning Corrections and Error Models for Geometry-based Visual Odometry Methods
论文作者
论文摘要
本文提出了这样一个想法，即深度学习方法可用于补充经典的视觉探针仪，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，可以忠实地学习和补偿视觉探针过程固有的偏见，并且与概率损耗函数相关的学习结构可以共同估计残差错误的完整协方差矩阵，定义一个错误模型，以捕获该过程的异质性。关于自主驾驶图像序列的实验评估了同时改善视觉探光仪并估计与其输出相关的误差的可能性。
This paper 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.
