Improved likelihood models for probabilistic localization based on range scans

Range sensors are popular for localization since they directly measure the geometry of the local environment. Another distinct benefit is their typically high accuracy and spatial resolution. It is a well-known problem, however, that the high precision of these sensors leads to practical problems in probabilistic localization approaches such as Monte Carlo localization (MCL), because the likelihood function becomes extremely peaked if no means of regularization are applied. In practice, one therefore artificially smoothes the likelihood function or only integrates a small fraction of the measurements. In this paper we present a more fundamental and robust approach, that provides a smooth likelihood model for entire range scans. Additionally, it is location-dependent. In practical experiments we compare our approach to previous methods and demonstrate that it leads to a more robust localization.

Authors

• Patrick Pfaff
• Christian Plagemann
• Wolfram Burgard

Keywords

• Uncertainty
• Robot sensing systems
• Monte Carlo methods
• Vehicles
• Robustness
• Sampling methods
• Robot localization
• Intelligent robots
• USA Councils
• Likelihood Model
• Probabilistic Localization
• Likelihood Function
• Range Of Sensors
• Entire Scan
• Robust Localization
• Covariance Matrix
• State Space
• Laser Beam
• Density Estimation
• Kalman Filter
• Distribution Measurements
• Particle Filter
• Covariance Function
• Sensor Model
• Liking Ratings
• Laser Ranging
• Gaussian Process Model
• Global Localization
• Number Of Beams
• Hitting The Wall
• Ray Casting
• Observation Likelihood
• Robot Pose
• Average Localization Error
• End Point
• Gaussian Density Function