I am working on an open source package for robot owners. I want to do a decent job of detecting when the robot is having movement problems. One of the problems the robot commonly has is that the back wheel gets "tucked underneath" in a bad way and makes it turn very slowly when on carpet. I believe that with a combination of accelerometer value inspection and (I hope) a relatively simple yet robust vision analysis technique, I will be able to tell when the robot is having this specific problem.
What I need is to be able to analyze two images, separated by about 1/2 second in time, and get a numerical value that tells about how close they are, but in a way that has some intelligence about the objects in the screen instead of just a simple color/hue/etc. analysis. I've heard of an algorithm called optical flow that is used in object and scene tracking, but I'm hoping I don't need something heavyweight.
Is there an algorithm/OpenCV function that can analyze two JPEG's and tell if they belong to the same scene and viewpoint, yet can also deliver a numerical monotonically increasing value that tells me rough how different they are? If I could get that numerical value and compare it to the number of milliseconds past, while examining the current accelerometer activity, I believe I can detect when the robot is having the "slow turn of death" problem.