# On Designing Consistent Covariance Recovery from a Deep Learning Visual Odometry Engine

arXiv: https://arxiv.org/abs/2403.13170 · HTML: https://mlsanity.com/paper/2403.13170/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Mar 2024.

## Built on by (most cited first, 1 of 1)

- [Learning Visual Information Utility with PIXER](https://mlsanity.com/paper/2409.13151/) (2024)

## Abstract (arXiv)

Deep learning techniques have significantly advanced in providing accurate visual odometry solutions by leveraging large datasets. However, generating uncertainty estimates for these methods remains a challenge. Traditional sensor fusion approaches in a Bayesian framework are well-established, but deep learning techniques with millions of parameters lack efficient methods for uncertainty estimation. This paper addresses the issue of uncertainty estimation for pre-trained deep-learning models in monocular visual odometry. We propose formulating a factor graph on an implicit layer of the deep learning network to recover relative covariance estimates, which allows us to determine the covariance of the Visual Odometry (VO) solution. We showcase the consistency of the deep learning engine's covariance approximation with an empirical analysis of the covariance model on the EUROC datasets to demonstrate the correctness of our formulation.