This week I worked with Kelton on object detection logic for the depth camera, specifically calibration. We are adopting the strategy of calibrating the surroundings upon system boot and constructing a “ground truth” frame with which we compare to real-time depth information. We assume that the ground in front of the user is flat during calibration, and found a way to model the depth information with linear regression (see charts in the team report.) For simplicity and to avoid the interference of obstacles on the side, we only take one pixel column in the middle of the frame and assume that it contains the correct information about a flat ground. We find that the reciprocal of the samples can be modeled by simple Ridge regression. We observe that the samples at the top of the frame are sometimes unstable, so we assign a smaller weight to those samples. Please note that this is only based on one pixel columns; the construction of the entire “ground truth” frame should be completed by tomorrow.
Next week I will focus on:
- finishing assembly of the physical belt with Alex; and
- implementation of obstacle detection & threat classification based on the “ground truth” frame with Kelton.