EKF-based simultaneous localization and mapping using laser corner-pattern matching

In this paper, we propose an extended Kalman filter (EKF)-based simultaneous localization and mapping (SLAM) method using laser corner-pattern matching for mobile robots. SLAM is one of the most important problems for mobile robots. To solve the problem, an EKF is often used. However, the existing EKF-based SLAM using landmarks method has the disadvantage of increased computation time, depending on the number of corner points. To improve SLAM computation time, we produce a corner pattern using classified and detected corner points. After producing corner patterns, the mobile robot's global position is estimated by matching them. The estimated position is used as a measurement model in the EKF. To evaluate the proposed method, we performed experiments in indoor environments. Experimental results from the proposed method show that it maintains accuracy and decreases computation time.