Simultaneous localization and mapping with unknown data association using FastSLAM

TLDR

The extended Kalman filter has dominated SLAM for fifteen years but suffers from quadratic complexity and data‑association sensitivity, whereas FastSLAM, a Rao‑Blackwellized particle filter, scales logarithmically with landmarks and can handle larger environments. The study aims to demonstrate that FastSLAM outperforms the EKF under ambiguous data association and to show how incorporating negative information improves map accuracy. We compare FastSLAM and EKF on a real‑world dataset with varying odometric noise and incorporate negative information into FastSLAM to enhance map estimation. FastSLAM substantially outperforms the EKF in environments with ambiguous data association.

Abstract

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.