Gaussian mixture models for probabilistic localization

One of the key tasks during the realization of probabilistic approaches to localization is the design of a proper sensor model, that calculates the likelihood of a measurement given the current pose of the vehicle and the map of the environment. In the past, range sensors have become popular for mobile robot localization since they directly measure distance. However, in situations in which the robot operates close to edges of obstacles or in highly cluttered environments, small changes in the pose of the robot can lead to large variations in the acquired range scans. If the sensor model used does not appropriately characterize the resulting fluctuations, the performance of probabilistic approaches may substantially degrade. A common solution is to artificially smooth the likelihood function or to only integrate a small fraction of the measurements. In this paper we present a more fundamental and robust approach which uses mixtures of Gaussians to model the likelihood function for single range measurements. In practical experiments we compare our approach to previous methods and demonstrate that it yields a substantially increase in robustness.

Authors

• Patrick Pfaff
• Christian Plagemann
• Wolfram Burgard

Keywords

• Robot sensing systems
• Mobile robots
• Robustness
• Vehicles
• Monte Carlo methods
• Filters
• Uncertainty
• Particle measurements
• Robotics and automation
• USA Councils
• Mixture Model
• Gaussian Mixture Model
• Probabilistic Localization
• Likelihood Function
• Probabilistic Approach
• Mobile Robot
• Range Of Sensors
• Sensor Model
• Environment Map
• Robot Pose
• Robot Localization
• Pose Changes
• Cluttered Environments
• End Point
• Laser Beam
• Expectation Maximization
• Kalman Filter
• Likelihood Model
• Mixture Components
• Particle Filter
• Global Localization
• Ray Casting
• Laser Ranging
• Position Tracking
• Average Localization Error
• Marked Regions
• Artificial Datasets
• Occupancy Grid