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Robust Monte Carlo Localization for Mobile Robots
Thomas Coffee
Based on:
Thrun S, Fox D, Burgard W, Dellaert F
Robust Monte Carlo Localization for Mobile Robots (2001)
Artificial Intelligence 128(1-2): 99-141
Image: Thrun et al. 2001
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The Problem of Localization
Image: Fox et al. 1999
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Tracking vs. Global Localization
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Global Localization Requires Multi-Modal Belief Representations
Image: Fox et al. 1999
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Global Localization for a Mobile Robot
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Multi-Hypothesis Kalman Filtering
Image: Roumeliotis et al. 2000
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Real Errors are Non-Gaussian!
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Markov Localization (ML)
Image: Fox et al. 1999
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Particle Filters to the Rescue!
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Monte Carlo Localization (MCL)
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Monte Carlo Localization (MCL)
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Monte Carlo Localization (MCL)
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How MCL Works
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Performance of MCL vs. ML
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Simulated Object Localization with MCL
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Better Sensors = Larger Errors?
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Object Localization Failure with MCL
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What Went Wrong?
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A Quick Fix for MCL
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Key Idea: Dual Sampling MCL
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Kernel Density Trees: Computing Densities from Particle Fields
Image: Fox et al. 2000
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Results of Dual MCL
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Mixture-MCL: Best of Both Breeds
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Results for Small Samples
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Results for the Kidnapping Problem
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Real Implementation of Mixture-MCL: Sampling Poses from Observations
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Mixture-MCL in Action
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Mixture-MCL in Action
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Mixture-MCL in Action
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Results for Real Implementation
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Is Mixture-MCL Efficient?
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Almost as Fast as Standard MCL!
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Advantages of Mixture-MCL
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Related Work and Applications
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Limitations and Assumptions
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Future Extensions to Mixture-MCL
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Thank you!
Image: Thrun et al. 1999
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