# Large-Scale Aircraft Pose Estimation System Based on Depth Cameras
## 1 Introduction
A new ultrathin marker point layout, based on a spatial encoding method, was used. The pose measurement accuracy of the model's pitch angle was less than 0.132\({}^{\circ}\), and the accuracy of the cross-rolling angle was less than 0.712\({}^{\circ}\)[13]. Optotrak(tm) developed a wind tunnel pose measurement device with a linear CCD camera and an infrared camera. The device exhibited good robustness and a 0.5\({}^{\circ}\) pose angle measurement accuracy, and has been successfully applied in wind tunnel measurement, motion tracking, and robotics. However, the marked point required for this method is large, and thus cannot be used to measure small models [14]. The principle of the laser-based method is that a laser estimates the change in distance by interference measurement. However, this method is complex, and due to the limitations of the reflected light angle, it can only be applied to the particular measurement scenario of small wind tunnels [15]. With regard to the non-contact measurement of model position and pose, a measurement system based on stereovision or structured light measurement has a high measurement accuracy, usually less than 0.1\({}^{\circ}\). However, the stereovision method requires pasting markers on the model, which will affect the aerodynamic performance of the model itself, and thus lead to inaccurate experimental results. With the increase of the working distance, the precision of distance measurement by structured light decreases sharply, so it is not suitable for the position and attitude measurement of large-sized aircraft. Three-dimensional (3D) point clouds have natural advantages in expressing dimensions, and are increasingly used in such fields as reverse engineering, precision manufacturing, and virtual reality [16; 17]. Compared with a two-dimensional image, a 3D point cloud can more accurately express the external shape and space of an object. At present, laser radar and depth sensors are used to obtain low-noise, high-resolution, and high-frame-rate point cloud data. Therefore, the use of 3D point cloud data for improved measurement has become a research hotspot. Additionally, depth cameras have a low cost and are being developed rapidly. In the measurement scenario of a large-sized aircraft model, the main difficulty is to satisfy the attitude measurement requirements of long-range and high-speed motion. In order to ensure measurement accuracy and good integration, depth cameras usually operate at less than 10 m. By combining multiple depth cameras to splice point clouds, the whole model of space covering the aircraft can be measured. It is particularly critical that the exposure time of the depth camera is less than 1 ms, so that the point cloud of the aircraft model does not cause a large amount of distortion due to its high-speed movement. This is an unavoidable problem for both traditional laser radar and structured light technology. The motion-capture camera depends on the external markers; once the markers are shifted, damaged, or blocked, the solution accuracy of the attitude algorithm is greatly reduced. The depth camera, on the other hand, does not rely on any external markers. Depth cameras have many advantages, such as being less influenced by the surface texture of the model, being unaffected by ambient light, having millimeter ranging accuracy, and so on [18]. Therefore, depth cameras are a suitable choice for the pose measurement of large-sized aircraft models. [PERSON] et al. [19] proposed a pose measurement method based on a depth camera and machine learning for robot assembly. Neural networks were used to learn simulated depth images and then transferred to real robots, obtaining an average pose estimation error of 2.16 mm and 0.64\({}^{\circ}\), leading to a 91% success rate for the robotic assembly of randomly distributed parts. [PERSON] et al. [20] designed an embedded system based on a ToF camera by using a laser as the driving light source.