A SLAM Method Based on Inertial/Magnetic Sensors and Monocular Vision Fusion

To solve the problem that the monocular vision SLAM (simultaneous localization and mapping) algorithm lacks the scale information and can't be used when the camera moves too fast, a SLAM method based on IMU (inertial measurement unit)/magnetic sensor and monocular vision fusion is proposed. Firstly, a fuzzy adaptive 9-axis attitude fusion algorithm is proposed for estimating the heading angle of IMU accurately. Then, a monocular ORB-SLAM2 (oriented FAST and rotated BRIEF SLAM2) algorithm is used, its scale factor is calculated by IMU, and scale transformation of its output pose information is performed. Finally, the loosely coupled method is adopted to fuse the poses of IMU and ORB-SLAM2 algorithm after scale transformation by Kalman filter. The experiments are carried out on a public dataset EuRoC, and the results show that the total root mean square error of the method is 5.73 cm. For further verification in the actual environment, an omnidirectional mobile platform is designed, and the test results show that the rotation angle error of the method is less than 5° and the total position RMSE is 9.76 cm compared with the pose data measurements from the lidar.