FastSLAM is a popular method to solve the problem of simultaneous localization and mapping. However, when the number of landmarks present in real environments increases, there are excessive comparisons of the measurement with all the existing landmarks in particles. As a result, the execution speed would be too slow to achieve the objective of real-time design. As an attempt to solve this problem, this paper presents an enhanced architecture for FastSLAM called computationally efficient SLAM (CESLAM), where odometer information is considered for updating the robot's pose in particles. When a measurement has a maximum likelihood with the known landmark in the particle, the particle state is updated before updating the landmark estimates. Simulation results show that the proposed algorithm in this paper can overcome the problem of the time-consuming process due to unnecessary comparisons and improve the accuracy of localization and mapping.