Advice needed for a final year project on wheelchair automation and control
To handle stereo imaging, using ONLY a visual camera, I believe the challenge will be the extraction of key image points that are UNAMBIGUOUS in both the left and right cameras. In the 1960s (60 years ago) Hans Morovac used his Points_Of_Interest, formed by a &quot;L&quot; shape of tape; the robot Shakey had two cameras, and had a mainframe computer (in a separate room) to search for the &quot;L&quot; markings. Correlation was then used, and Optic-Flow and Monocular disparity and binocular disparity.
By marking the objects with Black tape, this can be done, except when a 2nd object blocks the view of one of the cameras.
A fast enough camera Frame_Rate, and large &quot;L&quot; markings, would allow continuous tracking of the edges of the several &quot;L&quot;s in your scene, and the 3_D model would result in movement_vectors that predict how the edges of the &quot;L&quot;s will be moving. This prediction of movement is important for reducing the computational requirements.
This --- vector generation and tracking ---- is how I think the human eye is so effective with spotting sudden appearance of threats; ANY SURPRISES, any violation of the expected movement vectors beyond allowed occlusions in the 3_D model, and the human reacts with alarm and with foveation of the eyes.
This is a serious challenge, even for such as the ongoing development of SELF_DRIVING CARS. I've heard the reason the GOOGLE CARS are SO SLOW in turning corners --- is this 3_D problem. Part of the challenge is the constant BOUNCING UP and DOWN of the cameras with roads not being flat.
If you want to explore this, have only 2 or 3 LARGE &quot;L&quot; markings, and track those continuously. Make the markings LARGE, so there is continuous optic flow as the Black Tape Corner is seen to slowly move across the 3_D world model.
Until 5 years ago, simply getting the cameras precisely aligned, so you could perform a useful correlation between L/R images was a BIG DEAL. Only was 5 years ago was this alignment solved.
Our human eyes are continuously tracking, unlike electronic cameras. Thus what seems trivial to us humans, is very difficult for a machine.