Evaluations of different Simultaneous Localization and Mapping (SLAM) algorithms

Simultaneous Localization and Mapping (SLAM) algorithms with multiple autonomous robots have received considerable attention in recent years. In general, SLAM algorithms use odometry information and measurements from exteroceptive sensors of robots. The accuracy of these measurements and the performance of the corresponding SLAM algorithm directly affect the overall success of the system. This paper presents comparative performance evaluations of three Simultaneous Localization and Mapping (SLAM) algorithms using Extended Kalman Filter (EKF), Compressed Extended Kalman Filter (CEKF) and Unscented Kalman Filter (UKF). Specifically, it focuses on their SLAM performances and processing time requirements. To show the effect of CPU power on the processing time of SLAM algorithms, two notebooks and a netbook with different specifications have been used. Comparative simulation results show that processing time requirements are consistent with the computational complexities of SLAM algorithms. The results we obtained are consistent with the CPU power tests of independent organizations and show that higher processing power decreases processing time accordingly. The results also show that CEKF is more suitable for outdoor SLAM applications where there are a lot of natural and artificial features.