Probability for Robotics · Bayesian Filtering and Belief Estimation · M6-L1 · Intermediate
Histogram Localization
Bench log · Rover in the teaching lab
16:50. Rover is switched on in the east corridor of the depot: three identical doors, no idea where it is. It counts doors and drives one cell per command, but its wheels sometimes slip and its door detector sometimes cries wolf.
You will first see what a filter does when it trusts its models too much, then write the two steps of the histogram filter, and finally rescue it after a human picks Rover up and puts it down somewhere else without telling it.
Your task
Lab complete: post-mortem
You have passed every stage. Now connect it back to the theory and move on:
Output
What Python can I use?
Plain Python with math and the course library robotprob (for example from robotprob import Rng, mean, gaussian_pdf). NumPy is not available in the browser. Your code runs in a background worker: an endless loop is stopped after a few seconds and the page stays usable. It runs in your browser and is never sent anywhere.