一种惯性测量单元与相机固联安装下的对地定位方法

Abstract

Objective It is an effective means to identify and track the target using the airborne electrooptical platform with highresolution imaging equipment. However, in the actual combat process, it is challenging to meet the optimal observation conditions of nadir looking. Usually, it is necessary to quickly locate the remote target at a certain height, resulting in a large inclination of the photograph and a small angle between two observations. Furthermore, a motion camera s relative pose estimation is the key visual location technology based on an airborne electrooptical platform. Typical pose estimation methods include the visionbased pose estimation method and the visionbased pose estimation method with an inertial measurement unit (IMU). The latter introduces additional angle constraints based on the former. Under typical limited observation conditions such as a large inclination angle and small intersection angle, the camera s pose estimation accuracy is easily affected by the attitude angle error and the image point extraction error, which makes the target location accuracy difficult to meet application requirements. Therefore, it is of great significance to study target tracking and location under limited observation conditions to improve operational efficiency.Methods The unavoidable problem is that the measurement angle of the IMU drifts with time, and the calibration of the installation relationship between the IMU and the camera is cumbersome. Therefore, this paper proposes a ground target location method under the fixed installation of the IMU and the camera. This method does not require the installation relationship information between the calibration camera and the IMU. Firstly, two adjacent frames of images are extracted and matched through the scaleinvariant feature transformation (SIFT) matching algorithm. Secondly, the IMU installed in the fixed link is used to provide the relative rotation angle information for the motion camera. Combined with the robust estimation algorithm random sample consensus (RANSAC) algorithm, the relative rotation and translation of two adjacent frames are estimated according to a relative rotation angle and four corresponding image points. Then, combined with the external parameters of the first frame image, the external parameter matrix of any frame can be obtained. Finally, the projection matrix is used to intersect the spatial location of the target. Results and DiscussionsUnder the RANSAC framework, the smaller the minimum number of points required to solve the model, the same confidence level can be achieved with fewer iterations. Therefore, compared with other traditional algorithms, the algorithm proposed in this paper can significantly improve the computational efficiency under the same ratio of outliers (Fig. 3). In the simulation and flight experiment, the airborne electrooptical platform is 5 km away from the target and observes the target at an angle of 66.42°. Monte Carlo simulation analysis shows that the location accuracy of the proposed method is affected ...