# Deep Online Correction for Monocular Visual Odometry

arXiv: https://arxiv.org/abs/2103.10029 · HTML: https://mlsanity.com/paper/2103.10029/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 36 references that are in the map.
- First posted on arXiv 18 Mar 2021.

## Builds on (most cited first, 23 of 23)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Spatial Transformer Networks](https://mlsanity.com/paper/1506.02025/) (2015)
- [ORB-SLAM: a Versatile and Accurate Monocular SLAM System](https://mlsanity.com/paper/1502.00956/) (2015)
- [ORB-SLAM3: An Accurate Open-Source Library for Visual, Visual-Inertial and Multi-Map SLAM](https://mlsanity.com/paper/2007.11898/) (2020)
- [ORB-SLAM2: an Open-Source SLAM System for Monocular, Stereo and RGB-D Cameras](https://mlsanity.com/paper/1610.06475/) (2016)
- [Unsupervised Learning of Depth and Ego-Motion from Video](https://mlsanity.com/paper/1704.07813/) (2017)
- [Direct Sparse Odometry](https://mlsanity.com/paper/1607.02565/) (2016)
- [Unsupervised Scale-consistent Depth and Ego-motion Learning from Monocular Video](https://mlsanity.com/paper/1908.10553/) (2019)
- [D3VO: Deep Depth, Deep Pose and Deep Uncertainty for Monocular Visual Odometry](https://mlsanity.com/paper/2003.01060/) (2020)
- [DeepVO: Towards End-to-End Visual Odometry with Deep Recurrent Convolutional Neural Networks](https://mlsanity.com/paper/1709.08429/) (2017)
- [BA-Net: Dense Bundle Adjustment Network](https://mlsanity.com/paper/1806.04807/) (2018)
- [Depth from Videos in the Wild: Unsupervised Monocular Depth Learning from Unknown Cameras](https://mlsanity.com/paper/1904.04998/) (2019)
- [Self-supervised Learning with Geometric Constraints in Monocular Video: Connecting Flow, Depth, and Camera](https://mlsanity.com/paper/1907.05820/) (2019)
- [UnDeepVO: Monocular Visual Odometry through Unsupervised Deep Learning](https://mlsanity.com/paper/1709.06841/) (2017)
- [Deep Virtual Stereo Odometry: Leveraging Deep Depth Prediction for Monocular Direct Sparse Odometry](https://mlsanity.com/paper/1807.02570/) (2018)
- [Towards Better Generalization: Joint Depth-Pose Learning without PoseNet](https://mlsanity.com/paper/2004.01314/) (2020)
- [Visual Odometry Revisited: What Should Be Learnt?](https://mlsanity.com/paper/1909.09803/) (2019)
- [Self-Supervised Deep Visual Odometry with Online Adaptation](https://mlsanity.com/paper/2005.06136/) (2020)
- [A Survey on Deep Learning for Localization and Mapping: Towards the Age of Spatial Machine Intelligence](https://mlsanity.com/paper/2006.12567/) (2020)
- [CNN-SVO: Improving the Mapping in Semi-Direct Visual Odometry Using Single-Image Depth Prediction](https://mlsanity.com/paper/1810.01011/) (2018)
- [Self-Supervised Deep Pose Corrections for Robust Visual Odometry](https://mlsanity.com/paper/2002.12339/) (2020)

## Built on by (most cited first, 9 of 9)

- [From SLAM to Situational Awareness: Challenges and Survey](https://mlsanity.com/paper/2110.00273/) (2021)
- [Towards Scale Consistent Monocular Visual Odometry by Learning from the Virtual World](https://mlsanity.com/paper/2203.05712/) (2022)
- [A Vision-Centric Approach for Static Map Element Annotation](https://mlsanity.com/paper/2309.11754/) (2023)
- [CAMAv2: A Vision-Centric Approach for Static Map Element Annotation](https://mlsanity.com/paper/2407.21331/) (2024)
- [On the Coupling of Depth and Egomotion Networks for Self-Supervised Structure from Motion](https://mlsanity.com/paper/2106.04007/) (2021)
- [Pseudo-LiDAR for Visual Odometry](https://mlsanity.com/paper/2209.01567/) (2022)
- [Towards Accurate Ground Plane Normal Estimation from Ego-Motion](https://mlsanity.com/paper/2212.04224/) (2022)
- [Self-Supervised Geometry-Guided Initialization for Robust Monocular Visual Odometry](https://mlsanity.com/paper/2406.00929/) (2024)
- [Vision-based localization methods under GPS-denied conditions](https://mlsanity.com/paper/2211.11988/) (2022)

## Abstract (arXiv)

In this work, we propose a novel deep online correction (DOC) framework for monocular visual odometry. The whole pipeline has two stages: First, depth maps and initial poses are obtained from convolutional neural networks (CNNs) trained in self-supervised manners. Second, the poses predicted by CNNs are further improved by minimizing photometric errors via gradient updates of poses during inference phases. The benefits of our proposed method are twofold: 1) Different from online-learning methods, DOC does not need to calculate gradient propagation for parameters of CNNs. Thus, it saves more computation resources during inference phases. 2) Unlike hybrid methods that combine CNNs with traditional methods, DOC fully relies on deep learning (DL) frameworks. Though without complex back-end optimization modules, our method achieves outstanding performance with relative transform error (RTE) = 2.0% on KITTI Odometry benchmark for Seq. 09, which outperforms traditional monocular VO frameworks and is comparable to hybrid methods.