FastSLAM is a well-known algorithm with its purpose to process the simultaneous localization and mapping (SLAM). There are two main FastSLAM algorithms, i.e., FastSLAM 1.0 and FastSLAM 2.0. However, the speed of execution is too slow due to the superabundant comparisons of every single existing landmarks. Thus computationally efficient SLAM (CESLAM) was presented to deal with the problem and to achieve the goal of real-time processing design. Nevertheless, there is a great possibility that large errors may occur, because the original CESLAM only takes odometer information to estimate the robot's pose in particles. Therefore, this paper not only utilizes the odometer information but also the measurement information from sensors. Finally, simulation results are illustrated that the modified version of CESLAM algorithm can effectively ameliorate the accuracy of localization and mapping.