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Robot Localization II: The Histogram Filter
This is part 2 in a series of articles explaining methods for robot localization, i.e. determining and tracking a robot's location via noisy sensor measurements. You should start with the first part: Robot Localization I: Recursive Bayesian Estimation Idea The Histogram Filter is the most straightforward solution to represent continuous beliefs. We simply divide $dom(x_t)$ into $n$