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© sebastian thrun, CMU, 20001 CS226 Statistical Techniques In Robotics Monte Carlo Localization Sebastian Thrun (Instructor) and Josh Bao (TA) http://robots.stanford.edu/cs226 Office: Gates 154, Office hours: Monday 1:30-3pm
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© sebastian thrun, CMU, 20002 Bayes Filters Bayes [Kalman 60, Rabiner 85] x = state t = time m = map z = measurement u = control Markov
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© sebastian thrun, CMU, 20003 Bayes filters Linear Gaussian: Kalman filters (KFs, EKFs) Discrete: Hidden Markov Models (HMMs) With controls: Partially Observable Markov Decision Processes (POMDPs) Fully observable with controls: Markov Decision Processes (MDPs) With graph-structured model: Dynamic Bayes networks (DBNs)
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© sebastian thrun, CMU, 20004 Markov Assumption Past independent of future given current state Violated: Unmodeled world state Inaccurate models p(x’|x,u), p(z|x) Approximation errors (e.g., grid, particles) Software variables (controls aren’t random)
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© sebastian thrun, CMU, 20005 x t-1 utut p(x t |x t-1,u t ) Probabilistic Localization map m laser datap(z|x,m)
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© sebastian thrun, CMU, 20006 What is the Right Representation? Multi-hypothesis [Weckesser et al. 98], [Jensfelt et al. 99] Particles [Kanazawa et al 95] [de Freitas 98] [Isard/Blake 98] [Doucet 98] Kalman filter [Schiele et al. 94], [Weiß et al. 94], [Borenstein 96], [Gutmann et al. 96, 98], [Arras 98] [Nourbakhsh et al. 95], [Simmons et al. 95], [Kaelbling et al. 96], [Burgard et al. 96], [Konolige et al. 99] Histograms (metric, topological)
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© sebastian thrun, CMU, 20007 Particle Filters
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© sebastian thrun, CMU, 20008 Particle Filter Represent p ( x t | d 0..t,m) by set of weighted particles {x (i) t,w (i) t } draw x (i) t 1 from p(x t-1 |d 0..t 1,m ) draw x (i) t from p ( x t | x (i) t 1,u t 1,m ) Importance factor for x (i) t :
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© sebastian thrun, CMU, 20009 Monte Carlo Localization (MCL)
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© sebastian thrun, CMU, 200010 Monte Carlo Localization (MCL) Take i-th sample “Guess” next pose Calculate Importance Weights Resample
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© sebastian thrun, CMU, 200011 Monte Carlo Localization
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© sebastian thrun, CMU, 200012 Sample Approximations
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© sebastian thrun, CMU, 200013 Monte Carlo Localization, cont’d
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© sebastian thrun, CMU, 200014 Performance Comparison Monte Carlo localizationMarkov localization (grids)
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© sebastian thrun, CMU, 200015 What Can Go Wrong? Model limitations/false assumptions Map false, robot outside map Independence assumption in sensor measurement noise Robot goes through wall Presence of people Kidnapped robot problem Approximation (Samples) Small number of samples (eg, n=1) ignores measurements Perfect sensors Resampling without robot motion Room full of chairs (discontinuities)
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© sebastian thrun, CMU, 200016 Localization in Cluttered Environments
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© sebastian thrun, CMU, 200017 Kidnapped Robot Problem
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© sebastian thrun, CMU, 200018 Probabilistic Kinematics map m
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© sebastian thrun, CMU, 200019 Pitfall: The World is not Markov!
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© sebastian thrun, CMU, 200020 Error as Function of Sensor Noise sensor noise level (in %) error (in cm) 1,000 samples
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© sebastian thrun, CMU, 200021 dual mixed MCL Error as Function of Sensor Noise sensor noise level (in %) error (in cm)
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© sebastian thrun, CMU, 200022 Avoiding Collisions with Invisible Hazards Raw sensorsVirtual sensors added
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