【摘要】：隨著航天技術(shù)的飛速發(fā)展以及空間活動的不斷增多,以衛(wèi)星在軌裝配、故障維修等為目的的空間目標(biāo)在軌捕獲技術(shù)已經(jīng)成為航天技術(shù)領(lǐng)域重要的研究方向�？臻g非合作目標(biāo)的運(yùn)動參數(shù)估計與三維重建是空間目標(biāo)在軌捕獲技術(shù)領(lǐng)域的主要關(guān)鍵技術(shù)之一,具有重要研究價值與意義。本課題主要研究工作是基于雙目立體相機(jī)的圖像信息,實現(xiàn)對空間非合作目標(biāo)的運(yùn)動參數(shù)估計及三維重建。首先,構(gòu)建了基于雙目立體相機(jī)的三維點(diǎn)云獲取系統(tǒng)。對相機(jī)成像模型進(jìn)行描述并完成相機(jī)的標(biāo)定;采用Triclops立體視覺庫設(shè)計了初始三維點(diǎn)云的獲取方案,通過對點(diǎn)云進(jìn)行預(yù)處理和立體處理,獲取目標(biāo)的視差信息,并根據(jù)三角測量的方法計算出目標(biāo)的深度信息,進(jìn)而獲取目標(biāo)初始三維點(diǎn)云;研究了基于PCL的三維點(diǎn)云后處理方法,完成對目標(biāo)點(diǎn)云去噪、拼接和采樣。其次,利用由目標(biāo)點(diǎn)云計算出的粗糙空間目標(biāo)位姿作為輸入,研究了一種基于無跡Kalman濾波器的運(yùn)動參數(shù)估計算法,可以同時估計出空間目標(biāo)平移與旋轉(zhuǎn)的運(yùn)動參數(shù)。包括質(zhì)心的位移及速度、旋轉(zhuǎn)角速度、慣性主軸的姿態(tài)以及目標(biāo)的主慣量相對值。通過數(shù)值仿真結(jié)果表明,該方法對空間非合作目標(biāo)運(yùn)動參數(shù)的估計具有較高的魯棒性及精度。然后,研究了一種提高空間非合作目標(biāo)三維重建速度與準(zhǔn)確性的方法。研究了基于二次柵格的點(diǎn)云簡化算法,通過二次柵格對點(diǎn)云進(jìn)行空間劃分,采用k近鄰搜索算法搜尋數(shù)據(jù)點(diǎn)的k近鄰,進(jìn)而計算出點(diǎn)云的法向量信息,根據(jù)法向量之間的夾角來進(jìn)行點(diǎn)云的選擇性采樣,能夠保留目標(biāo)的幾何特征,最后利用Power Crust算法對目標(biāo)進(jìn)行表面重建。針對空間目標(biāo)中典型的噴嘴特征進(jìn)行了仿真研究,驗證該方法能夠在去除點(diǎn)云中大量冗余數(shù)據(jù)的同時,保留模型表面的基本幾何特征,實現(xiàn)對空間目標(biāo)點(diǎn)云快速、準(zhǔn)確的三維重建。最后,搭建了實驗平臺對空間非合作目標(biāo)運(yùn)動參數(shù)估計與三維重建算法進(jìn)行實驗驗證。使用機(jī)械臂抓取衛(wèi)星模型模擬其在空間中的運(yùn)動,通過對不同視角下采集到的目標(biāo)圖像信息進(jìn)行處理,最終獲取目標(biāo)的運(yùn)動參數(shù),并對獲取到的衛(wèi)星模型點(diǎn)云進(jìn)行簡化及三維重建,重塑其幾何外形。對實驗結(jié)果分析表明:本文的方法對空間非合作目標(biāo)運(yùn)動參數(shù)的估計具有較高的魯棒性及精度,并能夠?qū)崿F(xiàn)目標(biāo)點(diǎn)云快速、準(zhǔn)確的三維重建。
[Abstract]:With the rapid development of space technology and the increasing of space activities, satellite on-orbit assembly, fault maintenance and other space target on-orbit acquisition technology has become an important research direction in the field of space technology. Estimation of motion parameters and 3D reconstruction of space non-cooperative targets is one of the key technologies in the field of on-orbit acquisition of space objects, which has important research value and significance. Based on the image information of binocular stereo camera, the motion parameter estimation and 3D reconstruction of non-cooperative objects in space are realized in this paper. Firstly, a three-dimensional point cloud acquisition system based on binocular stereo camera is constructed. The camera imaging model is described and the camera calibration is completed. The acquisition scheme of initial 3D point cloud is designed by using Triclops stereo vision library. The parallax information of the target is obtained by preprocessing and stereoscopic processing of the point cloud, and the depth information of the target is calculated according to the method of triangulation. Then the initial 3D point cloud of the target is obtained. A 3D point cloud post-processing method based on PCL is studied. The target point cloud is de-noised, stitched and sampled. Secondly, a motion parameter estimation algorithm based on the unscented Kalman filter is proposed, which can estimate the motion parameters of the spatial target translation and rotation simultaneously by using the rough space target position and pose of the target cloud computing as the input. It includes the displacement and velocity of the center of mass, the angular velocity of rotation, the attitude of the inertial spindle and the relative value of the main inertia of the target. The numerical simulation results show that the proposed method is robust and accurate to the estimation of motion parameters of space non-cooperative targets. Then, a method to improve the speed and accuracy of 3D reconstruction of space non-cooperative targets is studied. In this paper, the point cloud simplification algorithm based on quadratic grid is studied. The point cloud is partitioned by quadratic grid, and k nearest neighbor search algorithm is used to search k-nearest neighbor of data point, and then the normal vector information of point cloud is calculated. According to the angle between normal vectors, the point cloud can be sampled selectively, and the geometric features of the target can be preserved. Finally, the Power Crust algorithm is used to reconstruct the surface of the target. The typical nozzle features in space target are simulated. It is verified that this method can remove a large amount of redundant data from the point cloud, while preserving the basic geometric features of the model surface, so that the point cloud of the space target can be quickly implemented. Accurate 3D reconstruction. Finally, an experimental platform is built to verify the motion parameter estimation and 3D reconstruction algorithm. The robot arm grabbing satellite model is used to simulate its motion in space, and the target motion parameters are obtained by processing the target image information collected from different angles of view. The point cloud of the satellite model is simplified and reconstructed, and its geometric shape is reconstructed. The experimental results show that the proposed method is robust and accurate for estimating the motion parameters of non-cooperative objects in space, and can realize fast and accurate 3D reconstruction of target point clouds.
【學(xué)位授予單位】：哈爾濱工業(yè)大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP391.41

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