Abstract-The Extended Kalman Filter (EKF) has been the de facto approach to the Sirnultanoous Localization and Mapping (SLAM) pmblem for nearly liften yean. However, the EKF has two serious deficiencies that prevent it fmm being applied to large, realword environments: quadratic complexity and sensitivity to failurn in data association. FastSLAM, an alternative approach based on the bo-BlackweUized Particle Filter, has been shown l o scale logarithmically with the number of landmarks in the map [lo]. This efficiency enables FastSLAM to be applied to environments far larger than could be handled by the EKF. In this paper, we will show that FastSLAM also substantially outperforms the EKF in envimnments with ambiguous data association. The performance of the two algorithms is compared on a real-world data set with various levels of odomelric noise. In addition, we will show how negative inlormation can be incorporated into FastSLAM in order to impmve the accuracy of the &mated map.
In REF the authors have shown how negative information can be incorporated into FastSLAM, a system that is alternative to the complex Extended Kalman Filter approach for robot localization.
Simultaneous localization and mapping with unknown data association using FastSLAM
2003 IEEE International Conference on Robotics and Automation (Cat. No.03CH37422)
2003 IEEE International Conference on Robotics and Automation (Cat. No.03CH37422)
Engineering Computer Science