基于视觉的微小型无人直升机位姿估计与目标跟踪研究
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摘要
无人机在军事和民用上都具有广阔的应用前景,近年来已成为全球范围内的研究热点之一。实现自主导航是无人机能够在实际中应用的前提。相比基于惯性传感器和全球定位系统的传统导航方式,基于视觉的导航方式实时感知环境信息,能够完成相对位姿估计、避障机动飞行和地面目标跟踪这类与环境相关的任务,越来越受到重视。本论文选择微小型无人直升机作为实验平台,针对无人直升机视觉导航中的位姿估计问题和地面目标跟踪问题展开研究。本论文的主要工作为：
第一章首先介绍了本论文的研究背景和选题意义,随后综述了国内外无人机视觉导航技术研究现状,把无人机位姿估计方法按照使用的原理进行分类,并对地面目标跟踪硬件系统和软件系统进行分析,最后阐明了本论文的主要研究内容和篇章结构。
第二章对相关的基础理论做简单介绍,首先阐述了常用坐标系和姿态表示方法,接着介绍了与射影几何、摄像机模型和彩色图像相关的知识,最后讨论了基于扩展卡尔曼滤波的同步定位与地图构建算法。
第三章针对微小型无人直升机在城市环境中飞行时的姿态估计问题,提出了一种在朝下拍摄的图像中检测建筑物侧面垂直地面的边缘直线的消影点的方法。消影点检测方法的具体做法是先用Hough变换检测边缘直线,再进行反向Hough变换同时求取消影点与垂直边缘直线。根据射影几何关系从消影点坐标直接解算无人直升机的姿态。实验结果表明该方法能可靠地检测出消影点,直接解算得到的姿态值是准确的。
第四章针对微小型无人直升机在高空飞行时的姿态估计问题,提出了一种在朝前拍摄的图像中检测地平线的方法。地平线检测方法的具体做法是先用Hough变换检测出较长的直线作为候选地平线,再结合暗原色先验图像中的区域信息确定地平线。继而在扩展卡尔曼滤波理论框架下,结合地平线观测模型与刚体旋转模型估计无人直升机的姿态,并通过预测地平线参数及其残差协方差来判断检测到的地平线是否正确,如果错误则进行纠正。实验结果表明该方法能准确可靠地检测出地平线。
第五章针对微小型无人直升机在近似悬停旋转时的姿态估计问题,对“视觉罗盘”——基于单目视觉同步定位与地图构建的姿态测量方法进行改进。微小型无人直升机具有较大的角加速度,需要提高运动模型中系统噪声方差的设定值才能使视觉罗盘继续工作,但是导致匹配计算量增大,匹配错误发生率提高。改进包括使用多分辨率路标选取策略初始化新路标,使用逐层主动搜索匹配算法对路标进行快速匹配。实验结果表明改进后的方法具有匹配计算量减少,匹配错误发生率降低的优点。
第六章针对微小型无人直升机在近地面飞行时的位姿估计问题,对基于单目视觉同步定位与地图构建的位姿估计方法进行改进。改进包括用路标簇表示多个同时初始化的路标来减少摄像机参数冗余,增强摄像机位姿约束作用；提高路标初始深度设定值以减少在滤波更新后出现负深度的几率；用2点随机抽样一致性数据关联算法应对摄像机多个轴上的角速度快速变化。实验结果表明改进后的单目视觉SLAM方法位姿估计精度可以达到自主飞行需要。
第七章针对微小型无人直升机的地面目标跟踪问题,搭建了一套微小型无人直升机地面目标跟踪硬件系统,并提出了一种基于均值漂移和似然图像椭圆亮斑检测的视觉跟踪算法。为硬件系统先后设计加工了两代云台,第一代云台采用传统两轴两框架结构,第二代云台采用两轴三框架结构,解决了两轴两框架结构云台转动中存在奇异点的问题。视觉跟踪算法用目标／混合区域面积加权的直方图频度比值作为似然度,以抑制背景对目标的干扰；将目标跟踪转变为似然图像椭圆亮斑检测,用均值漂移估计椭圆亮斑的中心位置,用椭圆高斯差分算子估计椭圆亮斑的方向角和长、短半轴尺度。实验表明新跟踪算法能对旋转变化和尺度变化的目标进行跟踪。
第八章对本论文的研究工作进行了总结,并对进一步的研究做了展望。
Unmanned Aerial Vehicles (UAVs), which are being widely used in both military and civil applications, have been one of the most hot research topics global wide in recent years. The implementation of autonomous navigation is the prerequisite for UAV to be applied in reality. In comparison with traditional navigation that relies on inertial sensors and global position system (GPS), vision based navigation always acquires environmental information in real time, and can manage tasks related with environment such as relative pose estimation, obstacle avoidance maneuver, surveillance and tracking, which makes it receive more and more attention. In this dissertation, a miniature unmanned helicopter (MUH) is selected as the main experimental platform, and some researches have been done to solve problems in vision-based navigation, including relative pose estimation and ground target tracking. The main work of this dissertation is listed below:
In chapter1, research background and significance of this dissertation are introduced. Firstly, domestic and foreign research on vision-based navigation of UAV is reviewed. Then, the vision based navigation methods are classified according to the theories they use, and both the hardware platform and software system for ground target tracking are analyzed. Finally, the research contents and organization of this dissertation are clarified.
In chapter2, a brief introduction to the related basic theories is made. Firstly, the usually used reference coordination definitions and attitude description methods are described. Then, the relative knowledge of projective geometry, camera model and color image is introduced. Finally, the simultaneous localization and mapping algorithm based on extended Kalman filter (EKF-SLAM) is discussed.
In chapter3, in order to estimate the attitude of MUH flying in urban environment, a method to extract the vanishing point of vertical edge lines belong to buildings in aerial images captured by downwards looking camera is proposed. This method firstly extracts edge lines using Hough transform, and then extracts the vanishing point and vertical edge lines simultaneously using inverse Hough transform. The attitude of MUH is directly calculated from vanishing point's coordinate according to projective geometry. Experimental results show that this method can extract vanishing point reliably, and the estimated attitude is accurate.
In chapter4, in order to estimate the attitude of high altitude flying MUH, a method to extract horizon line in aerial images captured by forwards looking camera is proposed. This method firstly extracts the longest lines as the candidates of horizon using Hough transform, and then determines the horizon by taking region information of the dark channel priori image into consideration. Attitude of MUH is estimated using an EKF that combines horizon observation model and rigid body rotation model. The predicted horizon's parameters and innovation are used for judging whether the extracted horizon is true. Correction will be carried out when the extracted horizon line is erroneous. Experimental results show that this method can extract horizon robustly.
In chapter5, in order to estimate the attitude of MUH hovering and rotating, modifications are made to a MonoSLAM-based attitude estimation method called "visual compass". Since MUH has large angular accelerations, the visual compass method can work only if the a priori system noise covariance in motion model is set a large value. But this results in high computational cost for matching and high rate of erroneous matches. A multi resolution landmark selection strategy is adopted for initializing new landmark. Correspondingly, an active search and match layer by layer algorithm is proposed for fast landmark matching. The modified visual compass method is proved superior in less computational cost and lower rate of erroneous matches by experimental results.
In chapter6, in order to estimate the pose of MUH flying close to ground, modifications are made to the pose estimation method that based on MonoSLAM. Instead of parameterising several simultaneously initialized landmarks individually, landmark clutter is used for less camera parameter redundancy and reinforced camera pose constraints. The initial depth of landmark is set a larger value for deducing the ratio of changing depth from positive to negative after EKF updating. A2point random sample consensus (RANSAC) data association algorithm is proposed to cope with situation when angular velocities around multi-axis of camera change rapidly. The accuracy of modified MonoSLAM-based pose estimation method fulfills the requirement for autonomous flight in field-experiment.
In chapter7, in order to track the ground object from a MUH, a hardware platform for surveillance and tracking is constructed. Two gimbals are designed and manufactured. The first one with two-axis two-frame is traditional, and the second one with two-axis three-frame can avoids system singularity encountered in two-axis two-frame gimbals. Besides, a visual tracking algorithm based on mean shift and elliptical blob detection in likelihood map is proposed. This algorithm uses object/mixed regions'area weighted ratio of histograms as likelihood to suppress the interference from background. In likelihood map, the object appears to be an elliptical blob. The position of blob is estimated using mean shift, while the orientation, semi-major axis and semi-minor axis of blob are determined by calculating the difference of elliptical Gaussian. Experimental results show that this algorithm can track object with both rotation and scale changes.
In chapter8, the current research work of this dissertation is summarized and some recommendations for further research are presented.
第一章首先介绍了本论文的研究背景和选题意义,随后综述了国内外无人机视觉导航技术研究现状,把无人机位姿估计方法按照使用的原理进行分类,并对地面目标跟踪硬件系统和软件系统进行分析,最后阐明了本论文的主要研究内容和篇章结构。
第二章对相关的基础理论做简单介绍,首先阐述了常用坐标系和姿态表示方法,接着介绍了与射影几何、摄像机模型和彩色图像相关的知识,最后讨论了基于扩展卡尔曼滤波的同步定位与地图构建算法。
第三章针对微小型无人直升机在城市环境中飞行时的姿态估计问题,提出了一种在朝下拍摄的图像中检测建筑物侧面垂直地面的边缘直线的消影点的方法。消影点检测方法的具体做法是先用Hough变换检测边缘直线,再进行反向Hough变换同时求取消影点与垂直边缘直线。根据射影几何关系从消影点坐标直接解算无人直升机的姿态。实验结果表明该方法能可靠地检测出消影点,直接解算得到的姿态值是准确的。
第四章针对微小型无人直升机在高空飞行时的姿态估计问题,提出了一种在朝前拍摄的图像中检测地平线的方法。地平线检测方法的具体做法是先用Hough变换检测出较长的直线作为候选地平线,再结合暗原色先验图像中的区域信息确定地平线。继而在扩展卡尔曼滤波理论框架下,结合地平线观测模型与刚体旋转模型估计无人直升机的姿态,并通过预测地平线参数及其残差协方差来判断检测到的地平线是否正确,如果错误则进行纠正。实验结果表明该方法能准确可靠地检测出地平线。
第五章针对微小型无人直升机在近似悬停旋转时的姿态估计问题,对“视觉罗盘”——基于单目视觉同步定位与地图构建的姿态测量方法进行改进。微小型无人直升机具有较大的角加速度,需要提高运动模型中系统噪声方差的设定值才能使视觉罗盘继续工作,但是导致匹配计算量增大,匹配错误发生率提高。改进包括使用多分辨率路标选取策略初始化新路标,使用逐层主动搜索匹配算法对路标进行快速匹配。实验结果表明改进后的方法具有匹配计算量减少,匹配错误发生率降低的优点。
第六章针对微小型无人直升机在近地面飞行时的位姿估计问题,对基于单目视觉同步定位与地图构建的位姿估计方法进行改进。改进包括用路标簇表示多个同时初始化的路标来减少摄像机参数冗余,增强摄像机位姿约束作用；提高路标初始深度设定值以减少在滤波更新后出现负深度的几率；用2点随机抽样一致性数据关联算法应对摄像机多个轴上的角速度快速变化。实验结果表明改进后的单目视觉SLAM方法位姿估计精度可以达到自主飞行需要。
第七章针对微小型无人直升机的地面目标跟踪问题,搭建了一套微小型无人直升机地面目标跟踪硬件系统,并提出了一种基于均值漂移和似然图像椭圆亮斑检测的视觉跟踪算法。为硬件系统先后设计加工了两代云台,第一代云台采用传统两轴两框架结构,第二代云台采用两轴三框架结构,解决了两轴两框架结构云台转动中存在奇异点的问题。视觉跟踪算法用目标／混合区域面积加权的直方图频度比值作为似然度,以抑制背景对目标的干扰；将目标跟踪转变为似然图像椭圆亮斑检测,用均值漂移估计椭圆亮斑的中心位置,用椭圆高斯差分算子估计椭圆亮斑的方向角和长、短半轴尺度。实验表明新跟踪算法能对旋转变化和尺度变化的目标进行跟踪。
第八章对本论文的研究工作进行了总结,并对进一步的研究做了展望。
Unmanned Aerial Vehicles (UAVs), which are being widely used in both military and civil applications, have been one of the most hot research topics global wide in recent years. The implementation of autonomous navigation is the prerequisite for UAV to be applied in reality. In comparison with traditional navigation that relies on inertial sensors and global position system (GPS), vision based navigation always acquires environmental information in real time, and can manage tasks related with environment such as relative pose estimation, obstacle avoidance maneuver, surveillance and tracking, which makes it receive more and more attention. In this dissertation, a miniature unmanned helicopter (MUH) is selected as the main experimental platform, and some researches have been done to solve problems in vision-based navigation, including relative pose estimation and ground target tracking. The main work of this dissertation is listed below:
In chapter1, research background and significance of this dissertation are introduced. Firstly, domestic and foreign research on vision-based navigation of UAV is reviewed. Then, the vision based navigation methods are classified according to the theories they use, and both the hardware platform and software system for ground target tracking are analyzed. Finally, the research contents and organization of this dissertation are clarified.
In chapter2, a brief introduction to the related basic theories is made. Firstly, the usually used reference coordination definitions and attitude description methods are described. Then, the relative knowledge of projective geometry, camera model and color image is introduced. Finally, the simultaneous localization and mapping algorithm based on extended Kalman filter (EKF-SLAM) is discussed.
In chapter3, in order to estimate the attitude of MUH flying in urban environment, a method to extract the vanishing point of vertical edge lines belong to buildings in aerial images captured by downwards looking camera is proposed. This method firstly extracts edge lines using Hough transform, and then extracts the vanishing point and vertical edge lines simultaneously using inverse Hough transform. The attitude of MUH is directly calculated from vanishing point's coordinate according to projective geometry. Experimental results show that this method can extract vanishing point reliably, and the estimated attitude is accurate.
In chapter4, in order to estimate the attitude of high altitude flying MUH, a method to extract horizon line in aerial images captured by forwards looking camera is proposed. This method firstly extracts the longest lines as the candidates of horizon using Hough transform, and then determines the horizon by taking region information of the dark channel priori image into consideration. Attitude of MUH is estimated using an EKF that combines horizon observation model and rigid body rotation model. The predicted horizon's parameters and innovation are used for judging whether the extracted horizon is true. Correction will be carried out when the extracted horizon line is erroneous. Experimental results show that this method can extract horizon robustly.
In chapter5, in order to estimate the attitude of MUH hovering and rotating, modifications are made to a MonoSLAM-based attitude estimation method called "visual compass". Since MUH has large angular accelerations, the visual compass method can work only if the a priori system noise covariance in motion model is set a large value. But this results in high computational cost for matching and high rate of erroneous matches. A multi resolution landmark selection strategy is adopted for initializing new landmark. Correspondingly, an active search and match layer by layer algorithm is proposed for fast landmark matching. The modified visual compass method is proved superior in less computational cost and lower rate of erroneous matches by experimental results.
In chapter6, in order to estimate the pose of MUH flying close to ground, modifications are made to the pose estimation method that based on MonoSLAM. Instead of parameterising several simultaneously initialized landmarks individually, landmark clutter is used for less camera parameter redundancy and reinforced camera pose constraints. The initial depth of landmark is set a larger value for deducing the ratio of changing depth from positive to negative after EKF updating. A2point random sample consensus (RANSAC) data association algorithm is proposed to cope with situation when angular velocities around multi-axis of camera change rapidly. The accuracy of modified MonoSLAM-based pose estimation method fulfills the requirement for autonomous flight in field-experiment.
In chapter7, in order to track the ground object from a MUH, a hardware platform for surveillance and tracking is constructed. Two gimbals are designed and manufactured. The first one with two-axis two-frame is traditional, and the second one with two-axis three-frame can avoids system singularity encountered in two-axis two-frame gimbals. Besides, a visual tracking algorithm based on mean shift and elliptical blob detection in likelihood map is proposed. This algorithm uses object/mixed regions'area weighted ratio of histograms as likelihood to suppress the interference from background. In likelihood map, the object appears to be an elliptical blob. The position of blob is estimated using mean shift, while the orientation, semi-major axis and semi-minor axis of blob are determined by calculating the difference of elliptical Gaussian. Experimental results show that this algorithm can track object with both rotation and scale changes.
In chapter8, the current research work of this dissertation is summarized and some recommendations for further research are presented.
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