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

arXiv: https://arxiv.org/abs/2007.14943 · HTML: https://mlsanity.com/paper/2007.14943/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Jul 2020.

## Built on by (most cited first, 4 of 4)

- [Loss it right: Euclidean and Riemannian Metrics in Learning-based Visual Odometry](https://mlsanity.com/paper/2401.05396/) (2023)
- [Deep Bayesian ICP Covariance Estimation](https://mlsanity.com/paper/2202.11607/) (2022)
- [Salient Sparse Visual Odometry With Pose-Only Supervision](https://mlsanity.com/paper/2404.04677/) (2024)
- [A Consistency-Based Loss for Deep Odometry Through Uncertainty Propagation](https://mlsanity.com/paper/2107.00366/) (2021)

## Abstract (arXiv)

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.