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Low cost motion capture system for room scale tracking

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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

Architectural Diagram

Dependencies

Install the pseyepy python library: https://github.com/bensondaled/pseyepy

This project requires the sfm (structure from motion) OpenCV module, which requires you to compile OpenCV from source1. This is a bit of a pain, but these links should help you get started: SFM dependencies OpenCV module installation guide

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 tab to ask questions.

My blog post has some more information about the drones & camera: joshuabird.com/blog/post/mocap-drones

This post 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

Footnotes

  1. ⚠️ The experimental 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.

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