An Efficient Computational Hybrid Filter to the SLAM Problem for an Autonomous Wheeled Mobile Robot

The using of an autonomous wheeled mobile robot (AWMR) that perform diverse processes in a numerous number of applications without human's interposition in an unknown environment is thriving, nowadays. An AWMR can search the environment, create an adequate map, and localizing itself into this map, by interpreting the environment, autonomously. The FastSLAM is a structure for simultaneous localization and mapping (SLAM) for an AWMR. The correctness and efficiency of the estimation of the FastSLAM often depend on the accurate a previous knowledge of the control and measurement noise covariance matrices. Also, inaccurate previous knowledge may seriously degrade their efficiency. One of the major causes of losing particle manifold is sample impoverishment in the FastSLAM. These cases of the most main problems. This paper presents a robust new method to solve these problems as called Hybrid filter SLAM. In this method, for learning the measurement and control noise co-variance matrices for increasing correctness and consistency are utilized Intuitionistic Fuzzy Logic System (IFLS). In order to optimize efficiency of sampling from Cuckoo Search (CS). The results of the simulation and experimental shown that the Hybrid filter SLAM is efficient than the FastSLAM that has less number of computations and good performance for the larger environment.