The use of robots that accomplish varied procedures in numerous applications in an area are thriving nowadays. robots can search the area, create a fit map, and localize itself into this map, via interpreting the area autonomously. An approach for simultaneous localization and mapping (SLAM) for robots is the UFastSLAM. The accurate a previous knowledge of the control and measurement noise covariance matrices are needed to he correctness and efficiency of the estimation of the UFastSLAM depend often on. Also, inexplicit past data may seriously go down their efficiency. One of the significant causes of missing particle multifold is sample insignificant in the UFastSLAM. This paper presents a novel heuristic method to solve these issues called Hybrid filter SLAM (Hf SLAM). In the proposed method, for tuning the measurement noise covariance matrices for increase of consistency and correctness are exploited in the Intuitionistic Fuzzy Logic System (IFLS). Moreover, we exploit to improve the efficiency of importance weight from Cuckoo Search Optimization (CSO). The Hf SLAM is more efficient than the UFastSLAM and FastSLAM that has fewer computations and good performance for the larger area based on the results of the simulation and experiment demonstrated.