Adaptive square-root transformed unscented FastSLAM with KLD-resampling

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.

KEYWORDS:

• FastSLAM
• Rao–Blackwellized particle filter
• Kullback–Leibler distance
• transformed unscented Kalman filter

Acknowledgment

We thank the anonymous reviewers for their constructive comments and helpful suggestions.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This paper was supported by National High Technology Research and Development Program of China [grant number 2014AA091301].

Notes on contributors

Weijun Xu

Weijun Xu received his B.Sc. degree from Zhejiang University, Hangzhou, Zhejiang, China, in 2007. He is currently a Ph.D. candidate of Zhejiang University. His major research fields are estimation and filtering, simultaneous localisation and mapping.

Rongxin Jiang

Rongxin Jiang received his B.Sc. and Ph.D. degrees from Zhejiang University, Hangzhou, Zhejiang, China, in 2002 and 2008, respectively. He is currently an associate professor with the Institute of Advanced Digital Technologies and Instrumentation, Zhejiang University. His major research fields are mobile robot navigation, computer vision and networking.

Li Xie

Li Xie received his B.Sc. and M.S. degrees from Zhejiang University, Hangzhou, China, in 1996 and 1999, respectively, where he is currently an associate professor with the Institute of Advanced Digital Technologies and Instrumentation. His major research fields are mobile robot navigation, wireless sensor networks and functional brain information processing.

Xiang Tian

Xiang Tian received the B.Sc. and Ph.D. degrees from Zhejiang University, Hangzhou, Zhejiang, China, in 2001 and 2007, respectively. He is currently an associate professor with the Institute of Advanced Digital Technologies and Instrumentation, Zhejiang University. His major research fields are signal processing and video coding.

Yaowu Chen

Yaowu Chen received his Ph.D. degree from Zhejiang University, Hangzhou, Zhejiang, China, in 1998. He is currently a professor and the director of the Institute of Advanced Digital Technologies and Instrumentation, Zhejiang University. His major research fields are embedded system, multimedia system and networking.