Visual-inertial tracking using Optical Flow measurements

Abstract:   Visual-inertial tracking is a well known technique to track a combination of a camera and an inertial measurement unit (IMU). An issue with the straight-forward approach is the need of known 3D points. To by-pass this, 2D information can be used without recovering depth to estimate the position and orientation (pose) of the camera. This Master's thesis investigates the feasibility of using Optical Flow (OF) measurements and indicates the benifits using this approach. The 2D information is added using OF measurements. OF describes the visual flow of interest points in the image plane. Without the necessity to estimate depth of these points, the computational complexity is reduced. With the increased 2D information, the 3D information required for the pose estimate decreases. The usage of 2D points for the pose estimation has been verified with experimental data gathered by a real camera/IMU-system. Several data sequences containing different trajectories are used to estimate the pose. It is shown that OF measurements can be used to improve visual-inertial tracking with reduced need of 3D-point registrations.