Simultaneous localization and mapping with unknown data association using fastSLAM

The extended Kalman filter (EKF) has been the de facto approach to the Simultaneous Localization and Mapping (SLAM) problem for nearly fifteen years. However, the EKF has two serious deficiencies that prevent it from being applied to large, real-world environments: quadratic complexity and sensitivity to failures in data association. FastSLAM, an alternative approach based on the Rao-Blackwellized Particle Filter, has been shown to scale logarithmically with the number of landmarks in the map. This efficiency enables FastSLAM to be applied to environments far larger than could be handled by the EKF. In this paper, we show that FastSLAM also substantially outperforms the EKF in environments with ambiguous data association. The performance of the two algorithms is compared on a real-world data set with various levels of odometric noise. In addition, we show how negative information can be incorporated into FastSLAM in order to improve the accuracy of the estimated map.

Authors

• Michael Montemerlo
• Sebastian Thrun

Keywords

• Simultaneous localization and mapping
• Particle filters
• Working environment noise
• Robot motion
• Robot sensing systems
• Noise level
• Mobile robots
• Motion measurement
• Covariance matrix
• Binary trees
• Unknown Associations
• Real-world Data
• Kalman Filter
• Local Problems
• Negative Information
• Particle Filter
• Extended Kalman Filter
• Number Of Landmarks
• Various Levels Of Noise
• Mutually Exclusive
• Probabilistic Model
• Conditional Independence
• High Uncertainty
• Motion Model
• Robot Control
• Importance Weights
• Log Odds Ratio
• Performance In Situations
• Robot Path
• Robot Pose
• Motor Noise
• Landmark Localization
• Derivative Weight
• Accurate Association