This week I implemented the new modeling method of using the narrower range of feature pixels from Oak-D’s feature tracker module to compute the mean squared error against the baseline. Additionally, the non-overlapping region in the frame between the two stereo cameras’ field of view is filtered out based on depth threshold.
Another minor update in the data processing pipeline is that the parsing in raspberry pi main is matched with the new data format of multiple ultrasonic sensor readings in one line of message.
Next week, I will test if the new mean squared error is interpretable and significant enough to detect ground-level obstacles from depth image. Meanwhile, I will explore the professor’s suggestion of somehow retrieving derivatives of pixels across time to calculate obstacle speed.