Simultaneously Learning Corrections and Error Models for Geometry-based Visual Odometry Methods

TL;DR

This paper demonstrates that deep learning can enhance classical visual odometry by learning bias corrections and error models, leading to improved accuracy and uncertainty estimation in autonomous driving scenarios.

Contribution

It introduces a learning architecture with a probabilistic loss to jointly correct biases and estimate full covariance error models in visual odometry.

Findings

Deep learning effectively learns biases in visual odometry.

Joint correction and error modeling improve odometry accuracy.

The approach estimates heteroscedastic uncertainty in autonomous driving sequences.

Abstract

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.