# Extending Monocular Visual Odometry to Stereo Camera Systems by Scale Optimization

## TL;DR

This paper introduces a computationally efficient method to extend monocular visual odometry to stereo systems by directly optimizing scale through photometric error minimization, improving accuracy and robustness in challenging environments.

## Contribution

It presents a novel scale optimization technique for stereo visual odometry that leverages direct photometric error minimization, enhancing accuracy without significant computational overhead.

## Key findings

- Demonstrates improved accuracy on KITTI dataset
- Shows robustness in environments with repetitive textures
- Maintains computational efficiency with minimal overhead

## Abstract

This paper proposes a novel approach for extending monocular visual odometry to a stereo camera system. The proposed method uses an additional camera to accurately estimate and optimize the scale of the monocular visual odometry, rather than triangulating 3D points from stereo matching. Specifically, the 3D points generated by the monocular visual odometry are projected onto the other camera of the stereo pair, and the scale is recovered and optimized by directly minimizing the photometric error. It is computationally efficient, adding minimal overhead to the stereo vision system compared to straightforward stereo matching, and is robust to repetitive texture. Additionally, direct scale optimization enables stereo visual odometry to be purely based on the direct method. Extensive evaluation on public datasets (e.g., KITTI), and outdoor environments (both terrestrial and underwater) demonstrates the accuracy and efficiency of a stereo visual odometry approach extended by scale optimization, and its robustness in environments with challenging textures.