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

Source: [Simultaneously Learning Corrections and Error Models for Geometry-based Visual Odometry Methods](<https://www.sota2.com/research/paper/68fa68091e209131cea19b24>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-27T06:03:45.482Z

Authors: Andrea De Maio, Simon Lacroix

Published: 2020-07-29T16:35:40.000Z

## 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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

- [Uncertainty Quantification on EuRoC VINS-Fusion](<https://www.sota2.com/research/sota/uncertainty-quantification-on-euroc-vins-fusion.md>): Uncertainty Quantification; EuRoC VINS-Fusion; LL: 21.33.

- [Uncertainty Quantification on EuRoC ORB-SLAM 3](<https://www.sota2.com/research/sota/uncertainty-quantification-on-euroc-orb-slam-3.md>): Uncertainty Quantification; EuRoC ORB-SLAM 3; Log-Likelihood: 31.46.

- [Uncertainty Quantification on EuRoC DPVO](<https://www.sota2.com/research/sota/uncertainty-quantification-on-euroc-dpvo.md>): Uncertainty Quantification; EuRoC DPVO; LL: 24.06.

- [Pose Correction on UnCal-Flight (Yaw Constant)](<https://www.sota2.com/research/sota/pose-correction-on-uncal-flight-yaw-constant.md>): Pose Correction; UnCal-Flight (Yaw Constant); RMSE (m): 0.044.

- [Uncertainty Quantification on UnCal-Flight Yaw Constant](<https://www.sota2.com/research/sota/uncertainty-quantification-on-uncal-flight-yaw-constant.md>): Uncertainty Quantification; UnCal-Flight (Yaw Constant); LL: 24.36.

- [Uncertainty Quantification on UnCal-Flight (Yaw Forward)](<https://www.sota2.com/research/sota/uncertainty-quantification-on-uncal-flight-yaw-forward.md>): Uncertainty Quantification; UnCal-Flight Yaw Forward; Log Likelihood (LL): 22.13.

- [Pose Correction on UnCal-Flight Yaw Forward](<https://www.sota2.com/research/sota/pose-correction-on-uncal-flight-yaw-forward.md>): Pose Correction; UnCal-Flight Yaw Forward; RMSE (m): 0.062.

- [Pose Correction on EuRoC](<https://www.sota2.com/research/sota/pose-correction-on-euroc.md>): Pose Correction; EuRoC; RMSE: 0.032.

- [Uncertainty Quantification on EuRoC MSCKF](<https://www.sota2.com/research/sota/uncertainty-quantification-on-euroc-msckf.md>): Uncertainty Quantification; EuRoC MSCKF; LL: 28.69.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2007.14943>)

[PDF](<https://arxiv.org/pdf/2007.14943.pdf>)