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Journal of Shanghai Jiaotong University(Science) >

2020 , Vol. 25 >Issue 1: 76 - 87

DOI: https://doi.org/10.1007/s12204-019-2146-7

Robust Variational Bayesian Adaptive Cubature Kalman Filtering Algorithm for Simultaneous Localization and Mapping with Heavy-Tailed Noise

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  • (1. School of Aeronautics and Astronautics, Shanghai Jiao Tong University, Shanghai 200240, China; 2. Department of Aerospace Engineering, Ryerson University, Toronto, Canada)

Online published: 2020-01-12

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Abstract

Simultaneous localization and mapping (SLAM) has been applied across a wide range of areas from robotics to automatic pilot. Most of the SLAM algorithms are based on the assumption that the noise is time- invariant Gaussian distribution. In some cases, this assumption no longer holds and the performance of the traditional SLAM algorithms declines. In this paper, we present a robust SLAM algorithm based on variational Bayes method by modelling the observation noise as inverse-Wishart distribution with "harmonic mean". Besides, cubature integration is utilized to solve the problem of nonlinear system. The proposed algorithm can e?ectively solve the problem of ˉltering divergence for traditional ˉltering algorithm when su?ering the time-variant obser- vation noise, especially for heavy-tailed noise. To validate the algorithm, we compare it with other traditional ˉltering algorithms. The results show the e?ectiveness of the algorithm.

Key words： simultaneous localization and mapping (SLAM); variational Bayesian (VB); heavy-tailed noise; robust estimation

Cite this article

ZHANG Zhuqing (张铸青), DONG Peng (董鹏), TUO Hongya (庹红娅), LIU Guangjun (刘光军), JIA He (贾鹤) . Robust Variational Bayesian Adaptive Cubature Kalman Filtering Algorithm for Simultaneous Localization and Mapping with Heavy-Tailed Noise[J]. Journal of Shanghai Jiaotong University(Science), 2020 , 25(1) : 76 -87 . DOI: 10.1007/s12204-019-2146-7

References

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