Particle Filter experiment (no ICE here!)

Sounds like another post on ICE and Softimage Particles, but not at all! This semester I took 2 of the online courses offered by Stanford (introduction to AI and Machine Learning), that was a great experiment that I’m glad will continue on next semester. If you have not yet, check out those courses: ai-class.com and ml-class.org.


particle filter in action

In the AI Course Sebastian Thurn introduced seemingly a key algorithm in Robotics: Particle Filter, which is used to derive the position of a robot based on subsequent observations of its environment. I decided to give it a try, also a good reason to play with HTML5 canvases. Although the core of the logic truly lies in a bit more than 10 lines of code, it ended up being a bit more to build all the environment around it. What I actually found the most challenging was to build the correlation between a particle’s observation and the robot’s one so that a proper weight could be derived.

Anyway, enough said. You can have a look at the experiment here: particle filter.

You can also follow some discussion on it on reddit.

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