An intelligent UFastSLAM with MCMC move step

This article may be used for research, teaching, and private study purposes. Any substantial or systematic reproduction, redistribution, reselling, loan, sub-licensing, systematic supply, or distribution in any form to anyone is expressly forbidden. The publisher does not give any warranty express or implied or make any representation that the contents will be complete or accurate or up to date. The accuracy of any instructions, formulae, and drug doses should be independently verified with primary sources. The publisher shall not be liable for any loss, actions, claims, proceedings, demand, or costs or damages whatsoever or howsoever caused arising directly or indirectly in connection with or arising out of the use of this material. FastSLAM is a framework for simultaneous localization and mapping (SLAM). However, FastSLAM algorithm has two serious drawbacks, namely the linear approximation of nonlinear functions and the derivation of the Jacobian matrices. For solving these problems, UFastSLAM has been recently proposed. However, UFastSLAM is inconsistent over time due to the loss of particle diversity that is caused mainly by the particle depletion in the resampling step and incorrect a priori knowledge of process and measurement noises. To improve consistency, intelligent UFastSLAM with Markov chain Monte Carlo (MCMC) move step is proposed. In the proposed method, the adaptive neuro-fuzzy inference system supervises the performance of UFastSLAM. Furthermore, the particle impoverishment caused by resampling is restrained after the resample step with MCMC move step. Simulations and experiments are presented to evaluate the performance of algorithm in comparison with UFastSLAM. The results show the effectiveness of the proposed method. 1. Introduction The simultaneous localization and mapping (SLAM) is a fundamental problem of mobile robots to perform autonomous tasks such as exploration in an unknown environment. It represents an important role in the autonomy of a mobile robot. The two key computational solutions to the SLAM problem are EKF (extended Kalman filter)-SLAM and FastSLAM.[1] The EKF-SLAM approach is the most popular one to solve the SLAM problem. However , EKF-SLAM suffers from two major problems: the computational complexity and data association.[1] Recently, the FastSLAM algorithm has been introduced as an approach to solve the SLAM problem.[1–3] The two versions of FastSLAM can be found in litera-tures, namely FastSLAM1.0 and FastSLAM2.0.[1,3] In the FastSLAM algorithm, particle filter (PF) is used to estimate the robot pose, and EKF is used to estimate the location of the landmarks.[1] The key feature of FastSLAM is that the …