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

doi:10.1007/s12204-019-2146-7

摘要/Abstract

摘要： 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.

关键词: simultaneous localization and mapping (SLAM), variational Bayesian (VB), heavy-tailed noise, robust estimation

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

中图分类号:

TP 242

ZHANG Zhuqing (张铸青), DONG Peng (董鹏), TUO Hongya (庹红娅), LIU Guangjun (刘光军), . Robust Variational Bayesian Adaptive Cubature Kalman Filtering Algorithm for Simultaneous Localization and Mapping with Heavy-Tailed Noise[J]. Journal of Shanghai Jiao Tong University (Science), 2020, 25(1): 76-87.

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 Jiao Tong University (Science), 2020, 25(1): 76-87.

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