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

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 timeinvariant 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 effectively solve the problem of filtering divergence for traditional filtering algorithm when suffering the time-variant observation noise, especially for heavy-tailed noise. To validate the algorithm, we compare it with other traditional filtering algorithms. The results show the effectiveness of the algorithm.