Visual Inertial Navigation For Micro Umanned Aerial Vehicle

Unlike ground vehicles, micro aerial vehicles have the 3D maneuverability, which enable them to perform a lot of risky tasks in environments where humans cannot get access to. For tasks such as surveillance, rescue and reconnaissance, micro unmanned aerial vehicles(UAV) must be able to navigate without any prior knowledge of the scene map. In order to solve the navigation problem for micro UAV in unstructured environments where there is no GPS signal, we investigate a position and attitude measurement method from monocular visual input, as well as an Extended Kalman filter(EKF) based on visual and inertial measurement unit(IMU). Experiments are performed to demonstrate the validity of the algorithms.First, due to the possibility of motion blur and lack of corner in the input image, various feature detectors are analyzed and tested on every image from a challenging image sequence. Sometimes, feature might gather in a small region of an image. To solve this problem, we apply the feature detector to each block of an image. As for the computation of essential matrix from point correspondences, classic 5-point algorithm is used in combination with the RANSAC algorithm to eliminate the contamination of outliers.The next topic is the computation of the 5 DOF pose matrix from the essential matrix. Triangulation of point correspondences gives scaled 3D point coordinates. Coordinates of two consecutive triangulated 3D points can be used to calculate the relative scale factor of one pose matrix with respect to another, which is then used to rescale the latter pose matrix. Finally, concatenation of relative pose matrices results in a pose matrix sequence of the camera with respect to the world reference frame. In addition, local bundle adjustment was used to minimize the motion estimation drift.Finally, visual inertial navigation algorithm for a micro UAV is designed to improve the dynamic performance of the ego-motion measurement from an onboard camera. It additionally provides PD position control for the UAV with a velocity measurement. Pros and cons are listed for two process model candidates, based on which the process model with a direct IMU input is selected for the EKF to be designed. The EKF estimates such states as UAV body position, velocity attitude, IMU biases, scale factor induced by visual odometer. Finally, the algorithm was tested on a dataset with IMU and image information.