This week, I focused on adapting our vision system to support fully wireless operation. Previously, the OAK-D Pro’s stereo depth pipeline required high USB bandwidth and stable power, which tied the robot to a wall outlet. To enable battery-powered operation, I replaced the stereo depth estimation with a hand-size-based distance estimation approach using only the RGB camera. This significantly reduces USB bandwidth and power consumption, allowing reliable operation on battery power. The system calculates distance from the pixel size of detected hand landmarks using a calibration lookup table with linear interpolation across multiple reference distances (0.5m to 2.5m). I also resolved a camera firmware issue by clearing the device flash using the DepthAI DeviceBootloader API, and switched from the V3 pipeline API to the V2 API for better stability.
I also delivered the final presentation this week, presenting our complete solution, component tradeoffs, and verification results. After the presentation, I continued calibrating the hand-size distance estimation by measuring hand landmark pixel sizes at known distances and iteratively tuning the interpolation table to minimize error within our 1–2.5m operating range.