An Extended Kalman Filter-Based Simultaneous Localization and Mapping Algorithm for Omnidirectional Indoor Mobile Robot

Simultaneous Localization And Mapping (SLAM) is the ability of an autonomous robot to localize itself within the surroundings, while also constructing a map. It has several applications in a variety of industries, especially in indoor navigation, Autonomous driving, and robotics. This research uses an omnidirectional mobile robot to present the SLAM algorithm based on Extended Kalman Filter (EKF) in an indoor environment. Usually, SLAM combines data from odometry and other sensors, in order to overcome the accumulating error due to the wheel sliding that usually appears when using the odometry alone. Thus, in this study a Kinect sensor is used along with an odometer sensor in an unknown environment to reach a predefined point with a free collusion path using three main fuzzy controllers as part of its navigation system. The first fuzzy controller is responsible for the goal-seeking problem, where the robot moves towards the goal, the second and third controllers are responsible for static and dynamic obstacles avoidance. The robot changes its direction to avoid the static obstacles and its direction and speed to avoid the dynamic obstacles. An EV3 Lego robot is used to verify the effectiveness of the proposed study. Results shows the effectiveness of the used algorithm; the decentralized algorithm simplifies the control process, which decreases the error with the help of EKF.