Visual Gyroscope for Accurate Orientation Estimation

Abstract

A visual gyroscope is a device which estimates camera 3D rotation using image input, in our case a monocular video. Contrary to traditional Structure-From-Motion (SFM) or visual SLAM, we address the case where only the rotation must be found, whereas no translation estimate is desired. That case can be solved without computing an explicit 3D map of the environment, thus avoiding computationally expensive bundle adjustment. Instead, a simple linear method is used to obtain globally consistent rotations from relative rotation estimates between image pairs. We show that the obtained camera orientations are accurate w.r.t. ground truth collected with a navigation-grade (dGPS-supported) IMU, and reach 3D bearing errors below 1 over a 1-minute time interval for >90% of all cases. Efficient computation is achieved by employing GPU-enabled feature extraction and matching. To warrant on-line performance for sequences of arbitrary lengths we run the global rotation estimation in a sliding-window fashion and show that the accuracy of the camera orientations obtained by chaining the partial solutions stays high. Finally, we compare the proposed visual gyroscope to a publicly available SFM software and experimentally demonstrate the importance of a very large field-of-view for accurate rotation estimation.