ArUco-assisted Autonomous Localization Method for Wall Climbing Robots

To overcome the shortcomings of the existing localization techniques for wall climbing robots in special environments, such as less texture, relatively closed, and strong magnetic interference environments, a novel localization solution by observing ground-fixed ArUco is proposed through a robot-carried fisheye camera, and an ArUco-assisted autonomous localization method (A-IEF) based on this solution with multi-sensor fusion of inertial measurement unit (IMU)/encoder/fisheye camera is implemented. The method firstly recognizes the ArUco and selects the key frames according to the ArUco position in the fisheye images. Then, the reprojection law of the ground-fixed ArUco corner in the fisheye image is studied, and the re-localization optimization is performed with the robot pose constraint. Secondly, the Jacobian matrix of the corner reprojection error with respect to the increment of robot position and attitude is derived in the key frame interval. Next, a multi-information fusion method based on error-state extended Kalman filter (ES-EKF) is designed to correct the heading angle and the position of the robot, using the displacement errors estimated by the encoder and the reprojection errors of the ArUco corners as the observation. Finally, tests are conducted on large steel components, and the experimental results show that the proposed method has higher localization accuracy, the position estimation errors are kept within 0.06 m, and the heading angle errors are kept within 3.7°. Compared with the ArUco-rectified method and the dead-reckoning method, the proposed method reduces the position error by 47% and the heading angle error by 68%, and can implement localization in weak-light environments.