# DeepVO: A Deep Learning approach for Monocular Visual Odometry

arXiv: https://arxiv.org/abs/1611.06069 · HTML: https://mlsanity.com/paper/1611.06069/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Nov 2016.

## Built on by (most cited first, 8 of 8)

- [Multimodal Fusion and Vision-Language Models: A Survey for Robot Vision](https://mlsanity.com/paper/2504.02477/) (2025)
- [MDN-VO: Estimating Visual Odometry with Confidence](https://mlsanity.com/paper/2112.12812/) (2021)
- [MambaVO: Deep Visual Odometry Based on Sequential Matching Refinement and Training Smoothing](https://mlsanity.com/paper/2412.20082/) (2024)
- [AFT-VO: Asynchronous Fusion Transformers for Multi-View Visual Odometry Estimation](https://mlsanity.com/paper/2206.12946/) (2022)
- [Multi-Camera Sensor Fusion for Visual Odometry using Deep Uncertainty Estimation](https://mlsanity.com/paper/2112.12818/) (2021)
- [SemanticSLAM: Learning based Semantic Map Construction and Robust Camera Localization](https://mlsanity.com/paper/2401.13076/) (2024)
- [RAM-VO: Less is more in Visual Odometry](https://mlsanity.com/paper/2107.02974/) (2021)
- [HySafe-AI: Hybrid Safety Architectural Analysis Framework for AI Systems: A Case Study](https://mlsanity.com/paper/2507.17118/) (2025)

## Abstract (arXiv)

Deep Learning based techniques have been adopted with precision to solve a lot of standard computer vision problems, some of which are image classification, object detection and segmentation. Despite the widespread success of these approaches, they have not yet been exploited largely for solving the standard perception related problems encountered in autonomous navigation such as Visual Odometry (VO), Structure from Motion (SfM) and Simultaneous Localization and Mapping (SLAM). This paper analyzes the problem of Monocular Visual Odometry using a Deep Learning-based framework, instead of the regular 'feature detection and tracking' pipeline approaches. Several experiments were performed to understand the influence of a known/unknown environment, a conventional trackable feature and pre-trained activations tuned for object classification on the network's ability to accurately estimate the motion trajectory of the camera (or the vehicle). Based on these observations, we propose a Convolutional Neural Network architecture, best suited for estimating the object's pose under known environment conditions, and displays promising results when it comes to inferring the actual scale using just a single camera in real-time.