Information
# 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
## 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)
](https://youtu.be/0ql20JKrscQ?si=jkxyOe-iCG7fa5th)
## Architectural Diagram
## 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)