基于单片机的水下机器人定位系统

水下机器人的定位系统利用超声波传感器获取距离信息,同时采用三点定位法计算出位置坐标.利用单片机存储时间信息和温度信息,并将这些信息实时传送到工控机程序完成定位.最后,通过实验获取了超声波的定位数据,并采用非线性优化的方法对数据进行了分析,得到两种不同发射传感器的定位精度,对以后改进系统和提高定位精度都有参考价值.     

关于移动机器人定位系统的研究——基于多传感器的机器人定位技术

在移动式机器人系统中,导航系统是机器人实现自治的核心,在导航系统中机器人定位系统又有着至关重要的作用.因为,只有基于精确定位的导航才是可靠的.考虑到目前的移动机器人系统都装备有多传感器,本文着重研究了基于多传感器的移动机器人定位技术.本定位系统首先获取里程计信息,按轨迹生成法得到机器人的粗定位,并在粗定位基础上进行局部感知环境预测,然后融合视觉传感器和距离传感器的信息,以得到较精确的机器人局部感知模型,以此 在移动式机     

基于卡尔曼与改进SVM移动机器人定位研究

用四轮差动驱动轮式机器人作为试验平台,以编码器和陀螺仪作为机器人的定位系统,建立了机器人的运动学方程。采用卡尔曼滤波器对两种传感器的数据进行融合,以减小编码器和陀螺仪的误差,再通过最小二乘支持向量机建立回归曲线模型,获得机器人的位姿信息。     

基于惯性传感器的集群移动机器人定位系统设计

为了对集群机器人的定位技术做进一步研究,本文对群体移动机器人的整体框架进行了设计,主要包括控制单元、通信模块和传感器单元等,重点对基于MPU9250惯性传感器的定位技术进行了研究,定位系统以ZigBee模块组成无线传感网络,用Arduino对MPU9250进行姿态数据获取并进行处理,利用ZigBee网络将位置信息传输到上位机.本文提出的机器人惯导定位技术对于解决机器人的定位问题具有一定的指导意义.     

基于多传感器融合的双轮差速机器人定位研究

针对单一传感器有一定的误差与使用场景限制的问题,往往需要通过融合滤波技术对多传感器的测量信息进行优势互补。为改善由惯性姿态测量单元(Inertial measurement unit, IMU)与轮式里程计（Wheel Odometry）组成的航迹推算（Dead Reckoning, DR）长期使用时产生不可忽略的积分误差,以及全球定位系统（Global Positioning System, GPS）在遇到信号被遮挡或者干扰的情况下将无法正常工作等情况所引起的问题,本研究结合GPS、IMU、轮式里程计三种传感器对双轮差速机器人进行定位,以起到互补的作用,使其能够适应更加多样化的环境;同时,我们定时使用GPS模块对DR推算位置信息进行修正,并比较IMU互补滤波解算法与DMP解算法,选取效果更好的方案获取机器人欧拉角,最后使用扩展卡尔曼滤波(Extended Kalman Filtering, EKF)算法对传感器数据进行融合处理,得到机器人的最优定位。结果表明,在采用了多传感器融合的方案之后,机器人的定位精确度较之只有单一传感器的方案有了显著的提升。     

移动机器人激光超声定位系统

本文提出了一种基于激光超声技术来实现移动机器人定位的方法。基于惯性导航系统的陀螺仪来提供机器人实时的方向和位置信息,新型的激光超声绝对定位系统消除机器人在惯性导航系统中方向和位置的累计误差。基于传感器、环境观测和机器人的运动建立了相应的模型,并以扩展卡尔曼滤波技术将多种传感器的信息进行融合,从而最终实现了移动机器人的精确定位。     

基于巡线机器人的货物定位系统的设计和实现

在仓储货物信息自动化管理的过程中,为实现仓库货物的准确定位,设计了一种基于巡线机器人技术的货物定位系统.在机器人巡线过程中,通过车载条码扫描设备对货物条形码进行扫描,在条形码响应的同时,对货位信息进行采集.并将采集信息以无线传输方式发送到上位机进行分析处理.针对采集过程中由于条形码响应时间造成的货位信息误差问题,采用了误差冗余算法.实验结果表明,通过这种算法实现了货物的准确定位.     

基于GIS的工程测绘机器人自动定位系统设计

根据传统定位系统在传递信息时采用惯性传感器,导致调试结果误差大的问题,设计了基于GIS的工程测绘机器人自动定位系统。根据工程测绘机器人自动定位系统总体结构,将系统分为硬件系统和软件系统两部分。通过s2c2440微处理器向ONPYHON480图像传感器中发送采集图像信号,经过135mm/F2.8全面幅远摄定焦光学镜头将图像信号转换为电子信号,通过直接内存存取系统传送到存储单元之中。设计电源电路为工程测绘机器人自动定位系统提供设备电力供应,避免电源本身所产生杂波对系统产生干扰。选择PDU模式收发短信息,利用GIS技术开发电子地图,实现工程测绘机器人自动定位。调试结果表明,该系统具有定位结果误差小的特点,静态测试点定位误差在3mm以内,动态测试点定位误差在6mm以内,辅助提升系统自动化程度。     

基于多传感器融合的机器人蒙特-卡洛定位决策

在复杂的不确定环境里,采用单一传感器对机器人进行定位时精度较低,并且易受干扰,可靠性较差。针对这一问题,先将激光测距仪和超声波传感器得到的观测信息利用平方根无迹卡尔曼滤波(SR-UKF)进行融合。根据更新的状态值和误差方差,构造出机器人蒙特—卡洛定位(MCL)的重要性密度函数,充分利用各种传感器采集的冗余信息,综合2种传感器各自的优点。仿真实验表明:基于多传感器融合的机器人蒙特—卡洛定位决策(SR-UKF-MCL)在定位精度和鲁棒性上都有较大的提高,证明了该种方法的可行性。     

矿井人员融合定位系统

针对基于传统接收信号强度指示(RSSI)指纹定位算法的井下人员定位系统在离线采样阶段指纹数据库采集工作量大、易受井下环境影响,基于行人航迹推算(PDR)算法的定位系统存在误差累计的问题,设计了一种基于改进RSSI指纹定位算法和PDR算法的矿井人员融合定位系统。该系统采用GS1011控制器和MPU9150惯性传感器构成智能终端,将采集的惯性传感器、RSSI和时间戳数据通过井下WiFi网络上传至地面监控中心定位服务器;定位服务器采用扩展卡尔曼滤波对RSSI指纹定位算法和PDR算法的定位信息进行融合,实现井下人员定位。试验结果表明,该系统平均定位误差为1.79m,小于单独采用RSSI指纹定位算法或PDR算法的系统定位误差,定位精度满足井下人员定位要求。     

Enhancing the accuracy of WLAN-based location determination systems using predicted orientation information

Indoor location determination has emerged as a significant research topic due to the wide-spread deployment of wireless local area networks (WLANs) and the demand for context-aware services inside buildings. However, prediction accuracy remains a primary issue surrounding the practicality of WLAN-based location determination systems. This study proposes a novel scheme that utilizes mobile user orientation information to improve prediction accuracy. Theoretically, if the precise orientation of a user can be identified, then the location determination system can predict that user’s location with a high degree of accuracy by using the training data of this specific-orientation. In reality, a mobile user’s orientation can be estimated only by comparing variations in received signal strength; and nevertheless the predicted orientation may be incorrect. Incorrect orientation information causes the accuracy of the entire system to decrease. Therefore, this study presents an accumulated orientation strength algorithm which can utilize uncertain estimated orientation information to improve prediction accuracy. Implementation of this system is based on the Bayesian model, and the experimental results indeed show the effectiveness of our proposed approach.     

水下机器人定位算法

水下机器人的定位信息一般应包括位置信息和航向信息。选用超声波传感器测距,再作圆弧求交点的方法可获取位置信息,给出2种计算位置的算法;水下环境复杂,机器人的航向信息难以获取,建立拟合机器人的历史位置信息的曲线,进而对曲线进行求导来近似代替机器人航向的航向估计模型。水下实验验证了定位算法的可行性及可靠性,位置定位绝对误差小于20 cm,航向定位绝对误差大都在10°以内,满足定位要求。并分析定位误差来源,给出航向算法的修正模型。     

Robust detection and tracking of annotations for outdoor augmented reality browsing

A common goal of outdoor augmented reality (AR) is the presentation of annotations that are registered to anchor points in the real world. We present an enhanced approach for registering and tracking such anchor points, which is suitable for current generation mobile phones and can also successfully deal with the wide variety of viewing conditions encountered in real life outdoor use. The approach is based on on-the-fly generation of panoramic images by sweeping the camera over the scene. The panoramas are then used for stable orientation tracking, while the user is performing only rotational movements. This basic approach is improved by several new techniques for the re-detection and tracking of anchor points. For the re-detection, specifically after temporal variations, we first compute a panoramic image with extended dynamic range, which can better represent varying illumination conditions. The panorama is then searched for known anchor points, while orientation tracking continues uninterrupted. We then use information from an internal orientation sensor to prime an active search scheme for the anchor points, which improves matching results. Finally, global consistency is enhanced by statistical estimation of a global rotation that minimizes the overall position error of anchor points when transforming them from the source panorama in which they were created, to the current view represented by a new panorama. Once the anchor points are redetected, we track the user's movement using a novel 3-degree-of-freedom orientation tracking approach that combines vision tracking with the absolute orientation from inertial and magnetic sensors. We tested our system using an AR campus guide as an example application and provide detailed results for our approach using an off-the-shelf smartphone. Results show that the re-detection rate is improved by a factor of 2 compared to previous work and reaches almost 90% for a wide variety of test cases while still keeping the ability to run at interactive frame rates.     

Orientation accuracy analysis of multiple satellite networks using epidemic model

Micro-navigation sensors provide position and orientation information for the network of satellites. Each micro-navigation sensor system consists of a global positioning system receiver, a solid-state inertial measurement unit and lasers. To maintain the orientation accuracy of the satellites in the network, an orientation information transfer (OIT) method was developed. Two laser links are established to align a satellite with low orientation accuracy from a satellite with high orientation accuracy. The OIT process is similar to the spread of an epidemic, which has been extensively studied in the epidemiology. Therefore, the information transfer process was analysed by applying a modified epidemic model. The relation between OIT process and the orientation accuracy of the satellites in the network was mathematically investigated. It is shown that the OIT using lasers greatly improves the orientation accuracy of all satellites in the network. Furthermore, the overall orientation accuracy of the satellite network can be improved by increasing the alignment rate or decreasing the decay rate, which are the parameters of the system. The simulation results verified the analysis of the system. The results showed the feasibility of using epidemic theory to analyse orientation accuracy of a satellite network. Finally, this OIT model will allow coordination of relative attitudes between satellites.     

Computing camera parameters using vanishing-line information from a rectangular parallelepiped

A new approach to camera calibration using vanishing line information for three-dimensional computer vision is proposed. Calibrated parameters include the orientation, the position, the focal length, and the image plane center of a camera. A rectangular parallelepiped is employed as the calibration target to generate three principal vanishing points and then three vanishing lines from the projected image of the parallelepiped. Only a monocular image is required for solving these camera parameters. It is shown that the image plane center is the orthocenter of a triangle formed by the three vanishing lines. From the slopes of the vanishing lines the camera orientation parameters can be determined. The focal length can be computed by the area of the triangle. The camera position parameters can then be calibrated by using related geometric projective relationships. The derived results show the geometric meanings of these camera parameters. The calibration formulas are analytic and simple to compute. Experimental results show the feasibility of the proposed approach for a practical application—autonomous land vehicle guidance.This work was supported by National Science Council, Republic of China under Grant NSC-77-0404-E-009-31.     

Hyperpatches for 3D model acquisition and tracking

Automatic 3D model acquisition and 3D tracking of simple objects under motion using a single camera is often difficult due to the sparsity of information from which to establish the model. We developed an automatic scheme that first computes a simple Euclidean model of the object and then enriches this model using hyperpatches. These hyperpatches contain information on both the orientation and intensity pattern variation of roughly planar patches on an object. This information allows both the spatial and intensity distortions of the projected patch to be modeled accurately under 3D object motion. Considering human tracking as a specific application, we show that hyperpatches can not only be computed automatically during model acquisition from a monocular image sequence, but that they are also extremely appropriate for the task of visual tracking     

强约束条件下环绕式相机标定方法

目的 在计算机视觉和摄影测量领域,经常应用多视角图像对场景进行高精度的三维重建。其中,相机内参数和相机间固定相对关系的高精度标定是关键环节,文章提出一种能够在强约束条件下快速进行相机标定的方法。方法 通过相机间6个相互独立的约束,充分利用系统的几何条件,确定固有关系,再以共线方程为基础推导强约束条件下的平差模型,并应用于自检校光束法平差,开展相邻立体相机的匹配,实现多相机系统的快速标定。结果 最后通过实验,验证了加了强约束条件后,加大了平差的多余观测数,提高了标定精度和鲁棒性。结论 建立了相机标定系统,提出了在强约束条件下快速进行相机标定的方法,展开了人体三维重建研究,并且该方法可推广到多个相机组成的多相机立体量测系统的标定中。     

Navigation function-based visual servo control

In this paper, the mapping between the desired camera feature vector and the desired camera pose (i.e., the position and orientation) is investigated to develop a measurable image Jacobian-like matrix. An image-space path planner is then proposed to generate a desired image trajectory based on this measurable image Jacobian-like matrix and an image-space navigation function (NF) (i.e., a special potential field function) while satisfying rigid body constraints. An adaptive, homography-based visual servo tracking controller is then developed to navigate the position and orientation of a camera held by the end-effector of a robot manipulator to a goal position and orientation along the desired image-space trajectory while ensuring the target points remain visible (i.e., the target points avoid self-occlusion and remain in the field-of-view (FOV)) under certain technical restrictions. Due to the inherent nonlinear nature of the problem and the lack of depth information from a monocular system, a Lyapunov-based analysis is used to analyze the path planner and the adaptive controller. Simulation results are provided to illustrate the performance of the proposed approach.     

小物体3维重建中影像与几何模型的配准

目的 近景摄影测量中的目标几何形状复杂,且拍摄影像的角度变化大,给影像与几何模型的配准带来了困难。传统单幅影像与几何模型配准的做法,由于缺乏自动粗配准的方法,效率相对较低。将多视影像首先统一坐标系的做法,在近景目标的复杂背景下也难以实现。方法 为此,将近景目标置于平面标定板上,利用相机标定的方法同时解算出影像的内外方位元素,实现多视影像坐标系的统一。然后人工选取3组以上同名点,做多视影像与几何模型的绝对定向,得到初始配准参数。最后使用多视影像与几何模型漫反射渲染图之间的归一化互信息作为相似性测度,用Powell非线性优化方法得到配准参数的精确值。结果 实验结果表明,使用标定板可以稳定地获取多视影像的内外方位元素,用绝对定向得到的配准参数进行影像和几何模型的交替显示仍然可以看到明显的裂缝,在经过互信息优化后裂缝现象得到明显改善。结论 多视影像与几何模型配准相比传统单幅影像与几何模型配准,人工选取同名点的工作量大大减少,由于人工选点存在误差,影响绝对定向的精度,使用归一化互信息作为测度进行非线性优化,可以获得更高的精度。