Wireless Applications of Mobile Egomotion Estimation with Computer Vision

In this paper a summary of our orientation estimation algorithm is introduced with a focus on application areas on mobiles and in wireless environments. The standard problem of using gyroscopes for orientation estimation is that integration of raw angular rates with non-zero bias will lead to continuous drift of the result angle. To examine the nature of this bias, a simple error model was constructed for the whole device in terms of inertial sensing. For eliminating the bias, a sensor fusion algorithm was developed using the benefits of optical flow from the camera of the device. Our orientation estimator and bias removal method is based on complementary filters, in combination with an adaptive reliability filter for the optical flow features. The feedback of the fused result is combined with the raw gyroscope angular rates to compensate the bias. Various measurements were recorded on a real device running the demanding optical flow onboard. Our results are tested and reliable, and are able to enhance different application from the field of physics-education to current robust unmanned vehicles.