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Particle filters were one of the hardest ideas to grasp in robotic state estimation. I learn better when I can watch the belief evolve so I built **Pf-visual** How does a robot know where it is when both motion and sensing are noisy?It’s an interactive visualization of probabilistic localization: hundreds of pose hypotheses that spread on the motion update, reweight against range measurements, and resample when the distribution gets skewed. You can run a "Guided Tour" that walks through the stor
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