Abstract: In order to solve the problem that the positioning error of the optical flow algorithm is too large in the case of large camera rotation angle and camera calibration positioning algorithm is unable to locate when the number of LEDs is insufficient, an optical flow-camera calibration fusion positioning algorithm that combines the camera calibration positioning algorithm and optical flow algorithm with Kalman filtering is proposed. In particular, the camera calibration and positioning algorithm is used for indoor positioning. Then the optical flow algorithm is introduced to compensate the calculation results of the camera calibration and positioning algorithm. Finally, the Kalman filter is used to fuse the positioning results of the two algorithms. An experimental platform is built to verify the positioning performance of the optical flow-camera calibration fusion positioning algorithm. The experimental results show that the proposed algorithm can solve the problem that the camera calibration positioning algorithm can not locate when the number of LEDs is insufficient, and can reduce positioning error of the optical flow algorithm in the case of large rotation angles. Specifically, the average positioning error of the camera at large rotation angles has been reduced from 6.86 cm to 1.01 cm.

Key words: camera calibration and positioning algorithm, optical flow algorithm, Kalman filter, accuracy, robustness