# Self-Supervised Deep Pose Corrections for Robust Visual Odometry

arXiv: https://arxiv.org/abs/2002.12339 · HTML: https://mlsanity.com/paper/2002.12339/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Feb 2020.

## Built on by (most cited first, 6 of 6)

- [Deep Learning for Visual Localization and Mapping: A Survey](https://mlsanity.com/paper/2308.14039/) (2023)
- [Deep Online Correction for Monocular Visual Odometry](https://mlsanity.com/paper/2103.10029/) (2021)
- [Towards Scale Consistent Monocular Visual Odometry by Learning from the Virtual World](https://mlsanity.com/paper/2203.05712/) (2022)
- [Scene-agnostic Pose Regression for Visual Localization](https://mlsanity.com/paper/2503.19543/) (2025)
- [On the Coupling of Depth and Egomotion Networks for Self-Supervised Structure from Motion](https://mlsanity.com/paper/2106.04007/) (2021)
- [Salient Sparse Visual Odometry With Pose-Only Supervision](https://mlsanity.com/paper/2404.04677/) (2024)

## Abstract (arXiv)

We present a self-supervised deep pose correction (DPC) network that applies pose corrections to a visual odometry estimator to improve its accuracy. Instead of regressing inter-frame pose changes directly, we build on prior work that uses data-driven learning to regress pose corrections that account for systematic errors due to violations of modelling assumptions. Our self-supervised formulation removes any requirement for six-degrees-of-freedom ground truth and, in contrast to expectations, often improves overall navigation accuracy compared to a supervised approach. Through extensive experiments, we show that our self-supervised DPC network can significantly enhance the performance of classical monocular and stereo odometry estimators and substantially out-performs state-of-the-art learning-only approaches.