水下定位系统传感器安装误差标定技术研究

提出了一种基于非线性参数估计的水下定位系统传感器安装误差标定技术.水下定位系统由声学传感器,GPS和船体姿态传感器组成,各传感器的坐标系并不能完全重合,需要对传感器之间的误差标定后进行修正才能够获得高精度的定位效果.已有的标定方法多基于高斯-牛顿迭代法,而当非线性系统模型线性化带来较大误差时,常规的基于高斯-牛顿迭代法的标定方法已无法达到高精度的要求,而非线性模型参数估的直接解法则掺人了三阶量,有效减小线性化的模型误差,提高了各传感器安装误差的标定精度.海试数据处理表明:定位精度可由1 m提高到0.7 m.     

基于人工神经网络的CCD数码相机颜色特征化

阐述了基于BP神经网络的数码相机特征化方法。采用不同的神经网络结构,建立了数码相机记录的RGB信息和原影像C IEXYZ色度信息之间的非线性对应关系。对NIKON D200数码相机进行了研究,通过实验得到了合理的神经网络结构为3—10—10—3。测试不同的训练样本和测试样本,达到的C IELAB平均色差和最大色差分别为1.9~2.2和6.7~7.4个色差单位。讨论了实验设备的重复性,同时,分析了样本数量对实验结果的影响。实验结果表明:对数码相机的特征化,可采用BP神经网络技术实现较高的精度。     

西红柿采摘机器人视觉系统的设计与研究

设计、开发了西红柿采摘机器人的双目立体视觉系统,为机器人自动化采摘作业提供条件。采用VFW方法进行了实时采集系统的设计;基于成熟西红柿与背景之间颜色特征的差异信息进行图像分割来识别成熟西红柿;在摄像头标定和形心匹配的基础上,通过三维立体重建获取了西红柿果实的空间位置信息。实验结果表明:视觉系统的成熟果识别率可达到98%,图像分割识别整个过程消耗平均时间0.21s;当工作距离小于500mm时,除个别奇异点,测试距离误差绝对值可控制在14mm以内,能较好满足实际工作需要。     

车载惯性导航仪快速初始对准技术

为了完成车载惯性导航仪在静基座下的对准与标定,提出采厢两位置对准方案.在惯性导航仪粗对准的基础上,建立了含有解算速度项的初始对准模型,并选取水平速度作为观测量.为了兼顾对准时间,采用自适应Kalman滤波器对失准角、惯性器件常值误差和随机误差进行实时估计,完成初始对准和标定.通过多次水平转台对准试验,表明在400 s对准时间内,方位角误差减小到0.7°以内,水平姿态角误差减小到0.04°以内,同时对系统中水平惯性器件的常值误差做出了估计,更加有利于导航精度的提高.     

Boosting集成回归在近红外光谱定量校正中的应用

针对物性参数和近红外光谱数据之间的回归模型的建立问题,基于建立一系列回归器的思想,给出了1种用于多变量校正的Boosting-PLS算法。每个(弱/基本)回归器均建立于原校正集的1个子集上,每个子集均通过原校正集带概率重复采样的方式得到,而样本的概率则由前1个回归器的预测误差确定。大误差的样本将增大概率,以便后续的回归器更集中地对其进行训练。最终的集成回归模型则为弱回归器的加权取中值。通过1个近红外应用实例和与偏最小二乘的比较,证实了Boosting-PLS算法的优良性能,所建校正模型更精确、更稳健,对过拟合不敏感。     

航空相机检测系统软件设计

为了检测航空相机工作是否正常,并可对有故障相机进行故障诊断,该文设计了一套自动测试软件系统。该系统能模拟飞机的任务管理系统与相机以ARINC429总线传输方式传输数据,并按相机记录信息的要求,提供时间、地速、高度等信息,以便控制相机的工作参数并记录相关信息,完成对航空相机性能的测试。     

分布式环境下的多摄像机标定

针对分布式环境下多摄像机的标定问题,我们提出了一种切实可行的多摄像机标定方法。标定过程仅需要各摄像机拍摄下包含激光点的图像即可。在整个标定过程中利用了所有图像的信息,因此比以往的方法具有更好的鲁棒性。整个标定操作过程方便,易于实现。实验结果表明,该方法是一种有效的多摄像机标定方法。     

Light-weight salient foreground detection for embedded smart cameras

An embedded smart camera is a stand-alone unit that not only captures images, but also includes a processor, memory and communication interface. Battery-powered, embedded smart cameras introduce many additional challenges since they have very limited resources, such as energy, processing power and memory. Computer vision algorithms running on these camera boards should be light-weight and efficient. Considering the memory requirements of an algorithm and its portability to an embedded processor should be an integral part of the algorithm design in addition to the accuracy requirements. This paper presents a light-weight and efficient background modeling and foreground detection algorithm that is highly robust against lighting variations and non-static backgrounds including scenes with swaying trees, water fountains and rain. Compared to many traditional methods, the memory requirement for the data saved for each pixel is very small in the proposed algorithm. Moreover, the number of memory accesses and instructions are adaptive, and are decreased depending on the amount of activity in the scene. Each pixel is treated differently based on its history, and instead of requiring the same number of memory accesses and instructions for every pixel, we require less instructions for stable background pixels. The plot of the number of unstable pixels at each frame also serves as a tool to find the video portions with high activity. The proposed method selectively updates the background model with an automatically adaptive rate, thus can adapt to rapid changes. As opposed to traditional methods, pixels are not always treated individually, and information about neighbors is incorporated into decision making. The results obtained with nine challenging outdoor and indoor sequences are presented, and compared with the results of different state-of-the-art background subtraction methods. The ROC curves and memory comparison of different background subtraction methods are also provided. The experimental results demonstrate the success of the proposed light-weight salient foreground detection method.     

FM radio for indoor localization with spontaneous recalibration

The position of mobile users has become highly important information in pervasive computing environments. Indoor localization systems based on Wi–Fi signal strength fingerprinting techniques are widely used in office buildings with an existing Wi–Fi infrastructure. Our previous work has proposed a solution based on exploitation of a FM signal to deal with environments not covered with Wi–Fi signal or environments with only a single Wi–Fi access point. However, a general problem of indoor wireless positioning systems pertains to signal degradation due to the environmental factors affecting signal propagation. Therefore, in order to maintain a desirable level of localization accuracy, it becomes necessary to perform periodic calibrations of the system, which is either time consuming or requires dedicated equipment and expert knowledge. In this paper, we present a comparison of FM versus Wi–Fi positioning systems and a combination of both systems, exploiting their strengths for indoors positioning. We also address the problem of recalibration by introducing a novel concept of spontaneous recalibration and demonstrate it using the FM localization system. Finally, the results related to device orientation and localization accuracy are discussed.     

基于红外光学原理的便携式甲烷报警仪

提出了基于红外光学原理的便携式甲烷报警仪的设计方案,详细介绍了报警仪的硬件电路设计、软件设计、光学气室结构、外形结构设计及标定技术。该报警仪采用红外光源、高精度干涉滤光片一体化热释电探测器和单光束双波长技术,配合镀金膜气室实现对CH4等气体的实时检测,具有不易老化、响应快、灵敏度好、性能稳定、抗干扰能力强等优点。