长航时无人机惯性/天文组合导航算法研究

针对捷联惯导/GPS组合导航系统的抗干扰性差、捷联惯导/星敏感器组合系统不能解决惯导位置发散的问题,提出了一种采用捷联惯导与基于间接敏感地平的天文导航相组合的长航时无人机导航新方案。分析了最小二乘微分校正天文定位方法对无人机的定位原理,设计了组合导航的滤波器,推导了系统的观测矩阵。仿真结果证明,组合方法能较好地实现无人机的定位定姿,具有较强的抗干扰性和较高精度,可以满足长航时无人机导航系统的要求。     

无人飞行器的北斗卫星组合导航算法研究

北斗卫星导航系统是中国自行研制的全球卫星定位与通信系统,为促进北斗卫星系统在导航方面的运用,降低我国导航方面对GPS的依靠,从而进一步降低成本,提高无人飞行器的导航精度,本文对捷联式惯性导航系统（INS）和北斗定位导航系统的组合导航算法进行了研究,通过惯性导航系统的原理和导航解算的过程,选择惯导系统和北斗系统的速度、位置差值作为观测量,建立组合导航系统的状态方程和观测方程,利用无迹卡尔曼滤波得到惯导系统状态量和惯性敏感器的误差,对惯导系统进行误差补偿,从而实现无人飞行器的高精度导航控制.并使用Matlab进行仿真,得出高精度的模拟输出轨迹.     

基于潜器空间运动的捷联式惯导系统误差仿真算法研究

针对捷联式惯性导航系统的导航定位误差受运载体运动特征影响的问题,首先以潜器空间六自由度解算模型为基础,建立了考虑风、浪、流影响因素的潜器空间操纵运动模型.通过分析动系、载体系和地理系的关系,建立了3个坐标系之间的坐标变换关系.而后以潜器空间运动模型为基础,设计了合适的变换算法,建立了潜器空间运动输出与捷联式惯导系统解算输入的合理衔接,并完成了变换算法的正确性仿真验证,从而建立了接近实际运动状态下的捷联惯导系统误差仿真算法.该算法对研究受运载体特殊工况影响的惯导系统误差传播规律,具有重要的实际研究意义.     

基于地球坐标系的全球惯性导航与组合导航方法

包含高纬度极区的全球导航是现代民用和远程军用飞机对导航系统的重要要求。极区地理环境的特殊性导致常规的指北方位惯导算法在极区不适用。针对全球导航中极区与非极区惯导算法不统一的问题,提出了以基于地球坐标系的惯导机械编排为内核,导航参数在地理坐标系和格网坐标系输出的全球导航方法。针对长航时惯导系统存在的误差累积问题,设计了基于地球坐标系的惯性/卫星紧组合导航滤波器。仿真实验表明,基于地球坐标系的惯导算法在极区与非极区的算法性能分别与格网算法和指北算法一致,惯性/卫星紧组合导航有效提升了导航精度。     

长航时捷联惯导重力扰动影响及补偿

从捷联惯导系统导航解算出发,分析了重力扰动矢量对捷联惯导误差的影响,并根据非均匀B样条最小二乘法,对局部区域内的重力扰动进行二维逼近,建立重力扰动模型,进而对捷联惯导系统进行补偿。仿真结果表明,非均匀B样条建模方法具有很好的精度,利用建立的重力扰动模型对长航时捷联惯导进行补偿可以有效地减小惯导误差。     

多惯组冗余系统安装误差的空中标定技术

以多组平行安装的捷联惯导构成的冗余系统为研究对象,提出了一种GPS辅助条件下惯导间安装误差的空中标定算法。该算法以其中一套惯导系统为安装基准,与GPS伪距、伪距率信息深组合后的导航结果为真实参考信息,标定前利用飞机机动提高基准惯导的姿态精度,标定开始时刻将基准惯导的导航信息装订给待标定惯导作为导航初值并进行姿态更新,利用待标定惯导的导航误差模型建立卡尔曼滤波器,量测量取为组合姿态与待标定惯导姿态的姿态误差,通过姿态匹配的方式对两套惯组之间安装误差进行标定。仿真结果显示,该方法不需要飞机做精确的角机动,只利用简单的摇翼和盘旋机动就可以对安装误差进行精确标定,且标定精度可达到角秒级水平。     

组合导航系统在四旋翼无人机上的实现

针对无人飞行器行业的快速发展和导航系统对飞行器的重要性,提出了组合导航系统的融合方案。介绍了捷联惯导系统的原理、姿态算法。通过对惯性传感器进行误差标定和补偿,利用扩展卡尔曼滤波器建立了INS/GPS组合导航系统。仿真实验表明,组合导航系统的工作性能要优于纯惯性导航系统,能够为飞行器提供较高的导航精度。最后,将这种组合导航系统在四旋翼无人飞行器上进行了实现。     

基于MEMS加速度计的无陀螺车辆导航技术

设计了一种基于低成本MEMS加速度计的无陀螺车载捷联惯导系统,将高精度的MEMS加速度计安放在载体非质心处,代替陀螺来测量载体角运动信息,实现车辆的GPS信号被遮挡时短时间内的导航定位定姿要求;仿真结果表明:对于300 s时间内导航的载体,无陀螺车载捷联惯导系统的位置误差达到了0.0002°,满足导航的精度要求。     

双轴旋转惯导系统的转位方案优化技术研究

针对旋转惯导系统导航性能受陀螺漂移调制特性影响的问题,以双轴旋转惯导系统为对象,推导了双轴依次旋转方式的漂移调制特性;通过傅里叶级数变换,提取了调制后陀螺漂移的关键分量;通过惯导误差方程求解和分析不同转、停时间条件下的定位误差特性,确定了优化的双轴转位方案。计算机比对仿真表明:优化的转位方案能有效提高双轴旋转捷联惯导的导航定位性能。     

考虑转台定位误差时的快速自对准方法研究

捷联惯导初始对准精度直接影响到惯导系统的工作精度,传统自对准方法对转台定位精度要求较高,转台存在定位误差时严重影响自对准精度。提出了一种考虑转台定位误差条件下的捷联惯导快速两位置自对准方法,根据对准位置与旋转过程中的惯导相邻时刻输出信息小幅慢变的特点,通过引入中间坐标系,实现实际旋转角度的精确跟踪和导航误差实时更新计算,辨识出初始姿态角误差,并根据对准精度要求对辨识结果迭代修正,从而实现动态条件下的高精度寻北。对该算法在静、动态环境下的寻北性能进行了实验验证,实验结果表明:静态环境下寻北精度的1倍标准差为18.3248″,动态环境下寻北精度的1倍标准差为32.633 07″,对准精度与计算速度均满足要求,能够有效克服转台定位误差对自对准精度的影响,具有一定的工程应用价值。     

基于深度残差和特征金字塔网络的实时多人脸关键点定位算法

针对人脸关键点定位算法需要分为人脸区域检测与单人脸关键点定位两个步骤,导致处理时间成倍增加的情况,提出一步到位的实时且准确的多人脸关键点定位算法。该算法将人脸关键点坐标生成对应的热度图作为数据标签,利用深度残差网络完成前期的图像特征提取,使用特征金字塔网络融合在不同网络深度中表征不同尺度感受野的信息特征,应用中间监督思想,级联多个预测网络由粗到精地一次性回归图中所有人脸的关键点,而无需人脸检测步骤。在保持高定位精度的同时,该算法完成一次前向传播只需要约0.0075 s （约每秒133帧）,满足了实时人脸关键点定位的要求,且在WFLW测试集中取得了6.06%的平均误差与11.70%的错误率。     

一种基于车联网架构的宽视野图像信息系统

基于无线物联网架构,提出了一种可实现车路信息实时采集、共享的宽视野图像信息系统。该系统应用GPS全球定位系统采集位置信息,使用图像传感器获得车辆周围图像;通过无线车辆网络将数据汇总;最后通过图像拼接算法构建宽视野图像信息。针对系统中的核心部分——构建宽视野图像信息,提出了一种基于SURF(speeded up robust features)图像特征的图像拼接算法。实验证明,该算法时间复杂度低,拼接效果良好,且系统具有良好的扩展性,可用于进一步开发车联网的智能应用。     

Real-time and accurate extrinsic camera parameter estimation using feature landmark database for augmented reality

In the field of augmented reality (AR), many kinds of vision-based extrinsic camera parameter estimation methods have been proposed to achieve geometric registration between real and virtual worlds. Previously, a feature landmark-based camera parameter estimation method was proposed. This is an effective method for implementing outdoor AR applications because a feature landmark database can be automatically constructed using the structure-from-motion (SfM) technique. However, the previous method cannot work in real time because it entails a high computational cost or matching landmarks in a database with image features in an input image. In addition, the accuracy of estimated camera parameters is insufficient for applications that need to overlay CG objects at a position close to the user's viewpoint. This is because it is difficult to compensate for visual pattern change of close landmarks when only the sparse depth information obtained by the SfM is available. In this paper, we achieve fast and accurate feature landmark-based camera parameter estimation by adopting the following approaches. First, the number of matching candidates is reduced to achieve fast camera parameter estimation by tentative camera parameter estimation and by assigning priorities to landmarks. Second, image templates of landmarks are adequately compensated for by considering the local 3-D structure of a landmark using the dense depth information obtained by a laser range sensor. To demonstrate the effectiveness of the proposed method, we developed some AR applications using the proposed method.     

Virtual 3D City Model for Navigation in Urban Areas

In this paper, we propose to study the integration of a new source of a priori information, which is the virtual 3D city model. We study this integration for two tasks: vehicles geo-localization and obstacles detection. A virtual 3D city model is a realistic representation of the evolution environment of a vehicle. It is a database of geographical and textured 3D data. We describe an ego-localization method that combines measurements of a GPS (Global Positioning System) receiver, odometers, a gyrometer, a video camera and a virtual 3D city model. GPS is often consider as the main sensor for localization of vehicles. But, in urban areas, GPS is not precise or even can be unavailable. So, GPS data are fused with odometers and gyrometer measurements using an Unscented Kalman Filter (UKF). However, during long GPS unavailability, localization with only odometers and gyrometer drift. Thus, we propose a new observation of the location of the vehicle. This observation is based on the matching between the current image acquired by an on-board camera and the virtual 3D city model of the environment. We also propose an obstacle detection method based on the comparison between the image acquired by the on-board camera and the image extracted from the 3D model. The following principle is used: the image acquired by the on-board camera contains the possible dynamic obstacles whereas they are absent from the 3D model. The two proposed concepts are tested on real data.     

基于虚拟直线的单目视觉坐标测量模型研究

在摄像机位置随意固定和测量点共面的约束条件下,基于单目视觉提出了一种基于虚拟直线的单帧图像物点坐标测量的新算法。用几何方法代替常规代数方法,回避了传统的通过求解旋转矩阵和平移向量来获取摄像机位置和姿态参数的过程。利用直线平行、垂直的先验知识,通过空间共线四点经过射影变换的交比不变性得到目标点的位置坐标信息。实验证明：该方法简单、可行,摄像机位置随意摆放,具有很强的可操作性、实用性,且精度较高。     

一种机器人导航中自然路标的匹配与跟踪方法

提出了一种基于SIFT和KLT算法的自然路标匹配与跟踪方法。该方法利用SIFT算子提取图像中自然路标的特征点集作为模板,然后将机器人采集图像中的SIFT特征点集与模板特征点集进行匹配,获取二者之间仿射关系,并解算自然路标在视野中的位置,为机器人自定位提供参考信息。机器人在运行过程中,将KLT算法与SIFT算法相结合对成功匹配的自然路标进行跟踪,较好地解决了SIFT算法效率低下的问题。实验结果表明该方法对自然路标具有较好的匹配和跟踪效果。     

基于视觉的SMT印刷钢网尺寸测量方法

为实现SMT钢网尺寸的精确自动测量,提出了一种基于Gerber文件信息的视觉测量方法。首先,采用NED公司的NUCLi 7K线扫描相机采集真实图像,并通过读取Gerber文件提取图形的形状及大小等信息。其次,通过坐标比例、旋转和平移变换建立Gerber文件图形信息坐标系和真实图像坐标系之间的坐标映射以确定图形在真实图像上的大致位置。然后,采用Canny算子和Gray moments算子实现图形边缘的像素级和亚像素级精确定位。最后,根据图形的亚像素边缘通过直线和圆等图元拟合方法实现图形尺寸的精确测量。实验中测量值的最大测量误差为0.0854pixel(0.91μm),均方差最大值为0.0282pixel(0.30μm),表明该测量方法具有稳定、可靠和精度高等特点,能有效满足钢网尺寸自动测量要求。     

Artificial landmark-based underwater localization for AUVs using weighted template matching

This paper deals with vision-based localization techniques in structured underwater environments. For underwater robots, accurate localization is necessary to perform complex missions successfully, but few sensors are available for accurate localization in the underwater environment. Among the available sensors, cameras are very useful for performing short-range tasks despite harsh underwater conditions including low visibility, noise, and large areas of featureless scene. To mitigate these problems, we design artificial landmarks to be utilized with a camera for localization, and propose a novel vision-based object detection technique and apply it to the Monte Carlo localization (MCL) algorithm, a map-based localization technique. In the image processing step, a novel correlation coefficient using a weighted sum, multiple-template-based object selection, and color-based image segmentation methods are proposed to improve the conventional approach. In the localization step, to apply the landmark detection results to MCL, dead-reckoning information and landmark detection results are used for prediction and update phases, respectively. The performance of the proposed technique is evaluated by experiments with an underwater robot platform and the results are discussed.     

基于UKF的红外目标空间定位方法

针对单站系统利用一次观测对目标进行定位,瞬时定位误差较大,提出了基于UKF的红外目标空间定位方法。基于针孔成像模型,借鉴主动视觉思想,通过控制红外热像仪运动拍摄目标图像,获取目标位置,并用GPS接收机测得拍摄点世界坐标。根据透视投影变换方程,借助站心地平坐标系和WGS-84坐标系建立红外目标空间定位的非线性系统模型,在此基础上引入UKF（Unscented Kalman Filtering）滤波算法进行空间定位。实验结果证明了该方法的可行性和有效性。     

Vision Based Position Control for MAVs Using One Single Circular Landmark

This paper presents a real-time vision based algorithm for 5 degrees-of-freedom pose estimation and set-point control for a Micro Aerial Vehicle (MAV). The camera is mounted on-board a quadrotor helicopter. Camera pose estimation is based on the appearance of two concentric circles which are used as landmark. We show that that by using a calibrated camera, conic sections, and the assumption that yaw is controlled independently, it is possible to determine the six degrees-of-freedom pose of the MAV. First we show how to detect the landmark in the image frame. Then we present a geometric approach for camera pose estimation from the elliptic appearance of a circle in perspective projection. Using this information we are able to determine the pose of the vehicle. Finally, given a set point in the image frame we are able to control the quadrotor such that the feature appears in the respective target position. The performance of the proposed method is presented through experimental results.