Difference between revisions of "Project stereo"

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==Possible Improvements==
==Possible Improvements==
For a more accurate comparison of the performance between the stereo and mono regimes, we considered recording 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.
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.

Revision as of 04:08, 23 March 2018

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

filler

Depth Perception

filler

Code

filler

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.