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https://github.com/jyjblrd/low-cost-mocap

Low cost motion capture system for room scale tracking
https://github.com/jyjblrd/low-cost-mocap

autonomous-robots bundle-adjustment computer-vision epipolar-geometry esp32 motion-capture motion-tracking quadcopter

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Low cost motion capture system for room scale tracking

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README 

        
# Low Cost Mocap (for drones)



### A general purpose motion capture system built from the ground up, used to autonomously fly multiple drones indoors



## YouTube Video

Watch this for information about the project & a demo!

[https://youtu.be/0ql20JKrscQ?si=jkxyOe-iCG7fa5th](https://youtu.be/0ql20JKrscQ?si=jkxyOe-iCG7fa5th)



[](https://youtu.be/0ql20JKrscQ?si=jkxyOe-iCG7fa5th)



## Architectural Diagram

![](https://github.com/jyjblrd/Mocap-Drones/blob/main/images/architecture.png?raw=true)



## Dependencies

Install the pseyepy python library: [https://github.com/bensondaled/pseyepy](https://github.com/bensondaled/pseyepy)



This project requires the sfm (structure from motion) OpenCV module, which requires you to compile OpenCV from source[^1]. This is a bit of a pain, but these links should help you get started: [SFM dependencies](https://docs.opencv.org/4.x/db/db8/tutorial_sfm_installation.html) [OpenCV module installation guide](https://github.com/opencv/opencv_contrib/blob/master/README.md)



[^1]: ⚠️ The experimental [`no-cv-sfm`](https://github.com/jyjblrd/Low-Cost-Mocap/tree/no-cv-sfm) branch removes this OpenCV-SFM dependency, however it is *completely* untested. It is recommended to first try use the `main` branch which is tested, however feedback and bug reports on the `no-cv-sfm` branch are greatly appreciated. 



install npm and yarn



## Runing the code



From the computer_code directory Run `yarn install` to install node dependencies 



Then run `yarn run dev` to start the webserver. You will be given a url view the frontend interface.



In another terminal window, run `python3 api/index.py` to start the backend server. This is what receives the camera streams and does motion capture computations.



## Documentation

The documentation for this project is admittedly pretty lacking, if anyone would like to put type definitions in the Python code that would be amazing and probably go a long way to helping the readability of the code. Feel free to also use the [discussion](https://github.com/jyjblrd/Mocap-Drones/discussions) tab to ask questions.



My blog post has some more information about the drones & camera: [joshuabird.com/blog/post/mocap-drones](https://joshuabird.com/blog/post/mocap-drones)



[This post](https://github.com/jyjblrd/Low-Cost-Mocap/discussions/11#discussioncomment-9380283) by gumby0q explains how `camera_params.json` can be calculated for your cameras.



## "Inside-Out" Multi-Agent Tracking (SLAM)

This motion capture system is an "outside-in" system, with external cameras tracking objects within a fixed space. There are also "inside-out" systems which use cameras on the drones/robots to determine their locations, not requiring any external infrastructure. 



My undergraduate dissertation presents such a system, which is capable of localizing multiple agents within a world in real time using purely visual data, with state-of-the-art performance. Check it out here: [https://github.com/jyjblrd/distributed_visual_SLAM](https://github.com/jyjblrd/distributed_visual_SLAM)