Project stereo

The goal of this project was to implement stereo vision in the Donkeycar framework using two USB cameras, training the neural network to drive our robot car around a track, and compare its performance to that of Donkeycar vehicle trained on the same track with only one camera.

Page still in progress.

Mounting USB Cameras to the Car

A 3D printed mount was created for the front of the car was created that would hold both USB cameras. The mount was designed so that the seats for both cameras would move as a single unit, pointing them at the same angle and minimizing the possibility of them pointing in different directions.

The mount also contains screw holes in the center that were intended to attach the original Picamera mount that we used earlier in the first half of the quarter. See the section "Possible Improvements" for more details on how this was meant to be used.

Training

The tawn_donkey.py template file provided by Tawn Kramer (here) already has code to implement stereo vision with the USB cameras. This code uses the pygame library, which does not come preinstalled in the Donkeycar framework. To install pygame, log onto the RPI and type in the following:

  Commands go here

After this, create a vehicle with the tawn_donkey.py template file, then change into the new directory:

  donkey createcar --path ~/stereoCar --template tawn_donkey
  cd stereoCar

Note, stereoCar is the name of new vehicle. Replace it in the commands above with the name of your choosing. Next, modify config.py to calibrate the steering and throttle as stated in the class tutorial, but with the addition of changing the following parameter:

  #CAMERA
  CAMERA_TYPE = "WEBCAM"
  IMAGE_W = 160
  IMAGE_H = 120
  IMAGE_DEPTH = 3
  CAMERA_FRAMERATE = DRIVE_LOOP_HZ

Make sure the cameras are plugged into the USB ports of the RPI. Start driving the car to collect data by typing the following:

 python manage.py drive --camera==stereo

Depth Perception

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Code

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Possible Improvements

For a more accurate comparison of the performance between the stereo and mono regimes, we considered using the Picamera concurrently with the two USB cameras to record data through all three cameras simultaneously. This would ensure that both the stereo and mono models were trained in the same conditions with similar data, giving us a mono model that would act as a proper control group. We didn't have the time to program thi functionality, but this might be something worth implementing in future projects.

The code we wrote for depth perception was functional, but running on the Raspberry Pi hardware was too slow. Future teams could work to improve our existing code, or even implement depth perception on faster hardware instead.