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Oculus Rift featured Crane control

Mount 2 cameras on a few servos, glue them to a crane. Read the position of the crane and display it into the field of view of the Rift.

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This project was created on 06/30/2014 and last updated 3 months ago.

Description
After I tried the Oculus Rift the first time in 2013 I immediately wanted to build something up with it. Relatively fast I decided to develop some kind of camera system which is mounted on 3 servos. The head movement of the user should be followed by the cameras.

So I started to build up a first prototype which was using a single camera. After it worked way better than I had imagined I bought 2 wide angle webcams which are now giving me also a stereoscopic view.

As I wanted to develop this project a bit further I made it an university project. For this we needed a possible industrial application. We decided to mount the system on top of a model toy crane. The crane can be controlled using a Joystick or gamepad and the current positions are displayed in the field of view of the user.

I also implemented a color based object recognition which can be used to estimate the distance to an object, also additional data is displayed once an object is recognized.
Details

The Software is written in C#. I use a PSoC5 and some other controllers to establish a USB-UART connection to the crane/servos.

As the Oculus Rift is distorting the image I also had to distort it to compensate it. It would use way to many resources to do that on the CPU. Therefore I implemented a pixel-shader to distort the images on the GPU.

Components
  • 1 × Dickie Mega Crane

Project logs
  • The final video

    3 months ago • 0 comments

    Check https://www.youtube.com/watch?v=WTd0HjDdIBI to see the final version.

  • Putting it together

    3 months ago • 0 comments

    Today we put everything together and gave it a try. 

    Its so much fun to use this thing and everything works quiet well.

    We have just a few issues.
    - As the cameras of course can not cross-eye (dont know how to say that in english), you will see very close object double.

    - We have some problems with the performance. With Object Recognition turned on, we have ~ 70-80% CPU load on a quad-core machine. I had to rearrange some thread priorities to stop the GUI from freezing. Its working but I want to optimize it. I maybe need to rethink how to process the images, maybe I also will try another framework for the cameras.

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