# UnDeepVO: Monocular Visual Odometry through Unsupervised Deep Learning

arXiv: https://arxiv.org/abs/1709.06841 · HTML: https://mlsanity.com/paper/1709.06841/

## Key facts

- Cited by 95 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Sep 2017.

## Built on by (most cited first, 30 of 95)

- [ESLAM: Efficient Dense SLAM System Based on Hybrid Representation of Signed Distance Fields](https://mlsanity.com/paper/2211.11704/) (2022)
- [Deep Patch Visual SLAM](https://mlsanity.com/paper/2408.01654/) (2024)
- [Unsupervised Scale-consistent Depth Learning from Video](https://mlsanity.com/paper/2105.11610/) (2021)
- [Monocular 3D Object Detection: An Extrinsic Parameter Free Approach](https://mlsanity.com/paper/2106.15796/) (2021)
- [Disentangling Object Motion and Occlusion for Unsupervised Multi-frame Monocular Depth](https://mlsanity.com/paper/2203.15174/) (2022)
- [Full Surround Monodepth from Multiple Cameras](https://mlsanity.com/paper/2104.00152/) (2021)
- [Differentiable SLAM-net: Learning Particle SLAM for Visual Navigation](https://mlsanity.com/paper/2105.07593/) (2021)
- [DiffPoseNet: Direct Differentiable Camera Pose Estimation](https://mlsanity.com/paper/2203.11174/) (2022)
- [MGNet: Monocular Geometric Scene Understanding for Autonomous Driving](https://mlsanity.com/paper/2206.13199/) (2022)
- [Attentive and Contrastive Learning for Joint Depth and Motion Field Estimation](https://mlsanity.com/paper/2110.06853/) (2021)
- [When the Sun Goes Down: Repairing Photometric Losses for All-Day Depth Estimation](https://mlsanity.com/paper/2206.13850/) (2022)
- [DF-VO: What Should Be Learnt for Visual Odometry?](https://mlsanity.com/paper/2103.00933/) (2021)
- [LIFT-SLAM: a deep-learning feature-based monocular visual SLAM method](https://mlsanity.com/paper/2104.00099/) (2021)
- [Deep Learning for Camera Calibration and Beyond: A Survey](https://mlsanity.com/paper/2303.10559/) (2023)
- [MAC-VO: Metrics-aware Covariance for Learning-based Stereo Visual Odometry](https://mlsanity.com/paper/2409.09479/) (2024)
- [XVO: Generalized Visual Odometry via Cross-Modal Self-Training](https://mlsanity.com/paper/2309.16772/) (2023)
- [Improving Monocular Visual Odometry Using Learned Depth](https://mlsanity.com/paper/2204.01268/) (2022)
- [Imperative Learning: A Self-supervised Neuro-Symbolic Learning Framework for Robot Autonomy](https://mlsanity.com/paper/2406.16087/) (2024)
- [From SLAM to Situational Awareness: Challenges and Survey](https://mlsanity.com/paper/2110.00273/) (2021)
- [iSLAM: Imperative SLAM](https://mlsanity.com/paper/2306.07894/) (2023)
- [Self-Supervised Monocular Depth Estimation by Direction-aware Cumulative Convolution Network](https://mlsanity.com/paper/2308.05605/) (2023)
- [Deep Learning for Visual Localization and Mapping: A Survey](https://mlsanity.com/paper/2308.14039/) (2023)
- [Reconstructing 4D Spatial Intelligence: A Survey](https://mlsanity.com/paper/2507.21045/) (2025)
- [Advancing Self-supervised Monocular Depth Learning with Sparse LiDAR](https://mlsanity.com/paper/2109.09628/) (2021)
- [Lightweight, Uncertainty-Aware Conformalized Visual Odometry](https://mlsanity.com/paper/2303.02207/) (2023)
- [Graph-based Thermal-Inertial SLAM with Probabilistic Neural Networks](https://mlsanity.com/paper/2104.07196/) (2021)
- [H-Net: Unsupervised Attention-based Stereo Depth Estimation Leveraging Epipolar Geometry](https://mlsanity.com/paper/2104.11288/) (2021)
- [JPerceiver: Joint Perception Network for Depth, Pose and Layout Estimation in Driving Scenes](https://mlsanity.com/paper/2207.07895/) (2022)
- [Self-supervised Visual-LiDAR Odometry with Flip Consistency](https://mlsanity.com/paper/2101.01322/) (2021)
- [Deep Online Correction for Monocular Visual Odometry](https://mlsanity.com/paper/2103.10029/) (2021)

## Abstract (arXiv)

We propose a novel monocular visual odometry (VO) system called UnDeepVO in this paper. UnDeepVO is able to estimate the 6-DoF pose of a monocular camera and the depth of its view by using deep neural networks. There are two salient features of the proposed UnDeepVO: one is the unsupervised deep learning scheme, and the other is the absolute scale recovery. Specifically, we train UnDeepVO by using stereo image pairs to recover the scale but test it by using consecutive monocular images. Thus, UnDeepVO is a monocular system. The loss function defined for training the networks is based on spatial and temporal dense information. A system overview is shown in Fig. 1. The experiments on KITTI dataset show our UnDeepVO achieves good performance in terms of pose accuracy.