Adaptive square-root transformed unscented FastSLAM with KLD-resampling. Issue 6 (26th April 2017)

• Record Type: Journal Article
• Authors: Xu, Weijun; Jiang, Rongxin; Xie, Li; Tian, Xiang; Chen, Yaowu
• Is Part Of: International journal of systems science. Volume 48:Issue 6(2017)
• Page Start: 1322
• Page End: 1330
• Publication Date: 2017-04-26
• Subjects: FastSLAM -- Rao–Blackwellized particle filter -- Kullback–Leibler distance -- transformed unscented Kalman filter; System analysis -- Periodicals; 003.3
• DOI: 10.1080/00207721.2016.1256449
• Languages: English
• ISSNs: 0020-7721

Abstract

ABSTRACT: The FastSLAM relies on particles sampled from the proposal distribution of underlying Rao–Blackwellized particle filter, and its performance is significantly influenced by the quality and quantity of the particles. In this paper, a new improved FastSLAM is proposed based on transformed unscented Kalman filter (TUKF) and Kullback–Leibler distance (KLD) resampling method. In the proposed algorithm, a square-root extension of TUKF is used to calculate the proposal distribution and to generate credible particles. In addition, during the resampling process, the minimum required number of particles is determined adaptively by bounding the KLD error between the sample-based approximation and true posterior distribution of the robot state. Both numerical simulations and real-world dataset experiments are used to evaluate the performance of the proposed algorithm. The results indicate that the proposed algorithm achieves higher estimation accuracy and computational efficiency than conventional approaches.