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

The Problem of Localization “Using sensory information to locate the robot in its environment is the most fundamental problem to providing a mobile robot with autonomous capabilities.” (Cox, 1991) Image: Fox et al. 1999

Tracking vs. Global Localization

Global Localization Requires Multi-Modal Belief Representations Image: Fox et al. 1999

Global Localization for a Mobile Robot Image: Thrun et al. 2001

Multi-Hypothesis Kalman Filtering Image: Roumeliotis et al. 2000

Real Errors are Non-Gaussian! Image: Thrun et al. 2001

Markov Localization (ML) Image: Fox et al. 1999

Particle Filters to the Rescue! Image: Thrun et al. 2001

Monte Carlo Localization (MCL) Image: Thrun et al. 2001

Monte Carlo Localization (MCL) Image: Thrun et al. 2001

Monte Carlo Localization (MCL) Image: Thrun et al. 2001

How MCL Works

Performance of MCL vs. ML Image: Thrun et al. 2001

Simulated Object Localization with MCL Image: Thrun et al. 2001

Better Sensors = Larger Errors? Image: Thrun et al. 2001

Object Localization Failure with MCL Image: Thrun et al. 2001

What Went Wrong?

A Quick Fix for MCL Image: Thrun et al. 2001

Key Idea: Dual Sampling MCL

Kernel Density Trees: Computing Densities from Particle Fields Recursive sampling above threshold Calculate densities by sum of weights in leaf divided by volume of leaf Equivalent to maximum likelihood estimation of piecewise constant density functions Like particle filters, concentrates effort in most useful regions Image: Fox et al. 2000

Results of Dual MCL Image: Thrun et al. 2001

Mixture-MCL: Best of Both Breeds Image: Thrun et al. 2001

Results for Small Samples Image: Thrun et al. 2001

Results for the Kidnapping Problem Image: Thrun et al. 2001

Real Implementation of Mixture-MCL: Sampling Poses from Observations

Mixture-MCL in Action Image: Thrun et al. 2001

Mixture-MCL in Action Image: Thrun et al. 2001

Mixture-MCL in Action Image: Thrun et al. 2001

Results for Real Implementation Image: Thrun et al. 2001

Is Mixture-MCL Efficient? Image: Thrun et al. 2001

Almost as Fast as Standard MCL! Image: Thrun et al. 2001

Advantages of Mixture-MCL

Related Work and Applications

Limitations and Assumptions

Future Extensions to Mixture-MCL

Thank you! Image: Thrun et al. 1999