This week we mostly worked on modeling the depth image. At first, the idea is to compute the mean squared error between baseline ground surface and the incoming image stream, and place a higher weight on closer data as illustrated below. However, it is discovered that the errors were relatively unchanged from a baseline surface to one with obstacle.
Now, the updated plan is to use Oak-D’s feature tracker module to pinpoint significant pixels to compare with their counterparts in baseline. Since these feature pixels represent qualitative difference from the baseline surface, we reasoned that the subsequent mean squared error difference will be separable from baseline. In terms of implementation, the RGB camera at the center will be used to align features detected from the left and right stereo cameras through Oak-D’s built-in alignment functionality.
Additionally, we are considering adding a Stop mode where all six coin motors will vibrate in cases where the user could use some help and not simply walk around an obstacle, e.g., when facing a staircase or wall. To achieve this, we have looked into classic computer vision algorithm and neural net model that can classify objects. This feature would also make more sense with audio feedback, which we would most likely leave as future work.