Template matching and tracking

I have been playing around recently with template matching and locking on to a target with the Robot Head MK 2. Parts 6 and 7 of the video series about this robot are available on Youtube.

 

After a training session where the user manually identifies and names a template, the robot can now match the template in the current image from the camera. The head will then move to centre the detected template in the robots field of view. The 3D position of the object is then calculated using the robot model and the reading from the sonar sensor. I have got as far as plotting these positions in a 3D matplotlib plot.

It was at this point that I noticed some problems. Its known that sonar sensors have a wide beam angle and this is particularly apparent when the robot is looking at something far away. The issue manifests itself as objects being detected as closer than they actually are, due to the wide beam of the sonar detecting objects that are either side of the head, closer than the target object. I could combat this with a different, probably more expensive type of sensor but I am going to try a different approach.

As explained in Part 7 above, I don’t really need a 3D model to show the robots head position, as this can be represented in pan/tilt coordinates. Whilst I have learned quite a bit from playing with template matching, its not the best method for matching scenes that the robot sees. It doesn’t work at different scales and its susceptible to false detection’s. I am going to try something different and I’ll be honest, I’m not sure what just yet! I have always been interested in how mapping works in the brain, and I think what I am trying to achieve  is similar to RatSLAM. On with some more reading and research and I will be back with an update again soon.

 

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