A robust and accurate aircraft pose estimation method is proposed in this paper. The aircraft pose reflects the flight status of the aircraft and accurate pose measurement is of great importance in many aerospace applications. This work aims to establish a universal framework to estimate the aircraft pose based on generic geometry structure features. In our method, line features are extracted to describe the structure of an aircraft in single images and the generic geometry features are exploited to form line groups for aircraft structure recognition. Parallel line clustering is utilized to detect the fuselage reference line and bilateral symmetry property of aircraft provides an important constraint for the extraction of wing edge lines under weak perspective projection. After identifying the main structure of the aircraft, a planes intersection method is used to obtain the 3D pose parameters based on the established line correspondences. Our proposed method can increase the measuring range of binocular vision sensors and has the advantage of not relying on 3D models, cooperative marks or other feature datasets. Experimental results show that our method can obtain reliable and accurate pose information of different types of aircraft.

Aircraft pose estimation is a necessary technology in aerospace applications, and accurate pose parameters are the foundation for many aerospace tasks. In this paper, we propose a novel pose estimation method for straight wing aircraft without relying on 3D models or other datasets, and two widely separated cameras are used to acquire the pose information. Because of the large baseline and long-distance imaging, feature point matching is difficult and inaccurate in this configuration. In our method, line features are extracted to describe the structure of straight wing aircraft in images, and pose estimation is performed based on the common geometry constraints of straight wing aircraft. The spatial and length consistency of the line features is used to exclude irrelevant line segments belonging to the background or other parts of the aircraft, and density-based parallel line clustering is utilized to extract the aircraft’s main structure. After identifying the orientation of the fuselage and wings in images, planes intersection is used to estimate the 3D localization and attitude of the aircraft. Experimental results show that our method estimates the aircraft pose accurately and robustly.

We propose a monocular trajectory intersection method to solve the problem that a monocular moving camera cannot be used for three-dimensional reconstruction of a moving object point. The necessary and sufficient condition of when this method has the unique solution is provided. An extended application of the method is to not only achieve the reconstruction of the 3D trajectory, but also to capture the orientation of the moving object, which would not be obtained by PnP problem methods due to lack of features. It is a breakthrough improvement that develops the intersection measurement from the traditional “point intersection” to “trajectory intersection” in videometrics. The trajectory of the object point can be obtained by using only linear equations without any initial value or iteration; the orientation of the object with poor conditions can also be calculated. The required condition for the existence of definite solution of this method is derived from equivalence relations of the orders of the moving trajectory equations of the object, which specifies the applicable conditions of the method. Simulation and experimental results show that it not only applies to objects moving along a straight line, or a conic and another simple trajectory, but also provides good result for more complicated trajectories, making it widely applicable.

Moving target tracking is a basic task in the processing of high speed photography. Despite its widely applications, Correlation tracking method can not adapt to the rotation and zoom of target and results in accumulation of tracking error. The Least Squares Image Matching(LSIM) method which is used in photogrammetry is introduced to moving target tracking, and a Weighted Least Squares Image Matching(WLSIM) based tracking algorithm is proposed. The WLSIM based algorithm sets weights according to the target's shape for the Least-Squares Image Matching Algorithm, as a result matching error produced by the background in the tracking window can be avoided. Experimental results are shown to demonstrate the robustness, efficiency and accuracy of the proposed algorithm.

In the task of lunar soil collection, estimating the volume of the collected soil is an important part of the sampling control of the lander. Due to the design constraints of the lander, there is no additional installation position for volume measurement equipment. To fully use the sensors already installed, a collected soil volume measurement method is designed in this paper based only on a single monitoring camera. This method uses a sequence of images of the collection area captured by the camera mounted on the acquisition arm to accurately reconstruct the terrain of the collection area surface before and after soil acquisition. Additionally, bi-temporal dense point clouds are reconstructed. Based on the area of change associated with soil collection, the constructed dense point clouds are compared according to the topographic characteristics of the area to estimate the volume of soil collected. Experiments show that the method is stable and reliable and can meet the requirements of actual measurement tasks.

Speckle fringe patterns of ESPI are full of high-spatial-frequency and high contrast speckle noise which defies normal process methods. Filtering with contoured windows has been proven to be an efficient approach to filter off the speckle noise while reserving the fringe patterns obtained by subtraction of two original speckle patterns. Furthermore, with contoured windows, the contoured correlation fringe pattern (CCFP) method proposed by the authors can derive highquality fringe patterns of ESPI with speckle-free, smooth, normalized and consistent fringes from two original speckle patterns. CCFP method can also extract the phase field with a single-step phase-shifting. Determination of contoured windows is a key step in CCFP method. The contoured windows used to be determined by fringe orientations only and this process would generate accumulated errors. In this paper, two new algorithms to determine the contoured windows according to the fringe intensity slope and the distance ratio to neighboring skeletons with the help of the local fringe direction are proposed. These new techniques can determine contoured windows more precisely and more robustly with no accumulated errors. Some applications of our new contoured windows are also presented.