Monocular Depth Constraint for Visual-Inertial Odometry

Overview

This repository offers an implementation of the Monocular Depth Constraint module, designed to augment the precision of Visual-Inertial Odometry (VIO) systems by integrating monocular depth estimations as constraints.

Key Features

• Advanced Monocular Depth Estimation: Utilizes cutting-edge algorithms for predicting depth from single images.
• Seamless Constraint Integration: Effortlessly merges depth constraints into the VIO optimization workflow.
• Improved Scale Consistency: Addresses the scale drift issues inherent to visual odometry systems.
• Enhanced Navigation Precision: Boosts the accuracy of navigation through additional geometric constraints.

Getting Started

For the loopback part of the framework, please refer to the open source work of the author kajo-kurisu. https://github.com/kajo-kurisu/D_VINS. To integrate the Monocular Depth Constraint module with your VIO system, follow these steps:

1. Clone the Repository:

   git clone https://github.com/index108/Monocular-Depth-Constraint.git

2. Install Dependencies: Ensure you have Python and the necessary libraries installed. See requirements.txt for the full list.

3. Configure Parameters: Adapt the parameters in config.py to align with your specific VIO system configuration.

4. Integrate with VIO: Incorporate the depth constraint module into your VIO optimization pipeline.

5. Run Optimization: Execute the VIO system with the integrated depth constraints.

6. Evaluate Results: Analyze the results to measure the improvement in accuracy.

Usage

The Monocular Depth Constraint module is designed for easy integration with existing VIO systems:

• Depth Estimation: Apply a pre-trained model to estimate depth from keyframe images.
• Constraint Formulation: Transform estimated depths into optimization constraints.
• Optimization: Use these constraints within the VIO system to refine scale and alignment during the optimization process.

Contributing

We welcome contributions to this project. For improvements or bug fixes, please open an issue or submit a pull request detailing your changes.

License

This project is released under the MIT License.

Acknowledgements

Gratitude to the open-source community and the pioneers in the field of monocular depth estimation and visual-inertial odometry for their foundational work.

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