Logistics

• 10 part1: Localizing

Localization

• I will be using these Localization Slides and information from this very useful and in depth paper
• Probabilistic algorithm
• See figure in PowerPoint
• Belief, Sense, Update … etc.
• But how is the belief represented, and how is the update done efficiently

AMCL Particles Display

Monte Carlo

• AMCL: Advanced Monte Carlo Localization** (I’ve also seen “Augmented” and “Adaptive”. Go figure.)
• Why Monte Carlo? That’s where the Casinos are!
• Algorithms that incorporate random guesses when a direct solution is hard or not feasible
• Diagram on the board how you would calculate Pi using a Monte Carlo Algorithm
• Note for PI it’s a very inefficient way to get an accurate result
• But it illustrates the idea of Monte Carlo estimation

Particle Filter

• Lets watch a short Video About Particle FIlters
• Markov localization means that the new state is dependent only on the previous state (and not the history) and that the probability distributions are
• Markov localization = state estimation from sensor data
• Instead of “solving” the equation for all data and all points
• Use a Monte-Carlo technique
• Generate a random collection of candidate locations
• Compute the motion
• Adjust the probability of each particle

Further references

• Video about Monte Carlo
• You might be interested in these Advanced Localization Slides.
• And another useful link: Where am I
• And another great explanation: Hector Mapping

Thank you. Questions?  (random Image from picsum.photos)