Kalman Filters + QNX madness

Today was a packed day, full of excitement. As the TA for computer vision, I had to give the lecture today since my professor is in Kyoto at the ICCV conference. My lecture was on SIFT, arguably the most important concept in computer vision. And so many glazed looks from the class….sigh….at least I don’t think it went too poorly. I debated recording it so I could analyze it later to see how badly I did, but didn’t get around to it. Next time….procrastination strikes successfully again. Interestingly enough, the back row was apparently the place to be. I had one friend fall asleep during my lecture and two others were apparently arguing on whether I was a controls or vision person. I personally maintain that I’m neither: while attempting to do both, I do neither well.

I am also working with the new visiting Spainish student in my lab on Kalman Filters and developing a model of Micron. The results so far are looking promising with a very basic Kalman filter with an identity A matrix and no inputs. It is able to filter stationary noise by several factors to an RMSE of 1-2 micros. Not bad, but then again we are using the Kalman filter under the most idealistic scenario. It will be interesting to see what happens when we add in a model of hand movements and the kinematics of the system. On an unrelated note, I spent some time working with Uma to get his new computer which uses PCI instead of ISA to work in the realtime operating system QNX. He is trying to interface with various electronics such as DACs mounted to PCI expansion cards. That still needs more work as we keep getting weird errors where functions compile and link just fine but then spit out “Error not implemented” when you run them. Oddnesses abound.

Simple Kalman Filtering
Simple Kalman Filtering