# Robust Edge-Direct Visual Odometry based on CNN edge detection and Shi-Tomasi corner optimization

arXiv: https://arxiv.org/abs/2110.11064 · HTML: https://mlsanity.com/paper/2110.11064/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 40 references that are in the map.
- First posted on arXiv 21 Oct 2021.

## Builds on (most cited first, 14 of 14)

- [ORB-SLAM: a Versatile and Accurate Monocular SLAM System](https://mlsanity.com/paper/1502.00956/) (2015)
- [ORB-SLAM2: an Open-Source SLAM System for Monocular, Stereo and RGB-D Cameras](https://mlsanity.com/paper/1610.06475/) (2016)
- [Holistically-Nested Edge Detection](https://mlsanity.com/paper/1504.06375/) (2015)
- [Direct Sparse Odometry](https://mlsanity.com/paper/1607.02565/) (2016)
- [Dense Extreme Inception Network: Towards a Robust CNN Model for Edge Detection](https://mlsanity.com/paper/1909.01955/) (2019)
- [Fast Edge Detection Using Structured Forests](https://mlsanity.com/paper/1406.5549/) (2014)
- [Computer Stereo Vision for Autonomous Driving](https://mlsanity.com/paper/2012.03194/) (2020)
- [Three-Filters-to-Normal: An Accurate and Ultrafast Surface Normal Estimator](https://mlsanity.com/paper/2005.08165/) (2020)
- [Canny-VO: Visual Odometry with RGB-D Cameras based on Geometric 3D-2D Edge Alignment](https://mlsanity.com/paper/2012.08228/) (2020)
- [Loop-box: Multi-Agent Direct SLAM Triggered by Single Loop Closure for Large-Scale Mapping](https://mlsanity.com/paper/2009.13851/) (2020)
- [Real-time stereo vision-based lane detection system](https://mlsanity.com/paper/1807.02752/) (2018)
- [Direct Monocular Odometry Using Points and Lines](https://mlsanity.com/paper/1703.06380/) (2017)
- [Edge-Direct Visual Odometry](https://mlsanity.com/paper/1906.04838/) (2019)
- [A Robust Pavement Mapping System Based on Normal-Constrained Stereo Visual Odometry](https://mlsanity.com/paper/1910.13102/) (2019)

## Abstract (arXiv)

In this paper, we propose a robust edge-direct visual odometry (VO) based on CNN edge detection and Shi-Tomasi corner optimization. Four layers of pyramids were extracted from the image in the proposed method to reduce the motion error between frames. This solution used CNN edge detection and Shi-Tomasi corner optimization to extract information from the image. Then, the pose estimation is performed using the Levenberg-Marquardt (LM) algorithm and updating the keyframes. Our method was compared with the dense direct method, the improved direct method of Canny edge detection, and ORB-SLAM2 system on the RGB-D TUM benchmark. The experimental results indicate that our method achieves better robustness and accuracy.