A Mobile Robot Indoor Positioning System Based on ArUco Array and Extended Kalman Filter

Abstract

To improve the accuracy and robustness of indoor positioning of robots and avoid the use of high-cost sensors such as LiDAR and high-precision Inertial Measurement Unit (IMU), a positioning method using an ArUco marker array as a manual marker and a camera as a global absolute positioning sensor is used. In addition, a fusion localization algorithm based on dynamic variance Extended Kalman filtering (EKF) is proposed to fuse visual information, wheel encoder information and IMU data, effectively improving the accuracy of indoor localization and reducing the influence of dynamic environment. Simulated experiments and indoor scene positioning tests are carried out to evaluate the viability of the positioning algorithm, and they are contrasted with the single sensor positioning approach. Experimental results show that the method can accurately localize indoor mobile robots.