Sonar Map Construction for Autonomous Mobile Robots Using a Data Association Filter

This paper presents a method for building a probability grid map for autonomous mobile robots with ultrasonic sensors using a data association filter (DAF). The method is based on evaluating the possibility that the acquired sonar data are all reflected by the same object. The DAF is able to associate data points with each other. Data affected by specular reflection are not likely to be associated with the same object, so they are excluded from the data cluster by the DAF, thereby improving the reliability of the data used for the probability grid map. Since the corrupted data are not used to update the probability map, it is possible to build a good quality grid map even in a specular environment. The DAF was applied to the Bayesian and the Orientation probability models, which are typical models used to build grid maps, to verify its effectiveness. Experimental results were also obtained using a mobile robot in a real-world environment.     

基于经典DSmT 的Sonar栅格地图创建

基于最近提出的一种在贝叶斯和DST扩展而来的信息融合算法DSmT(Dezert-Smarandache Theory),在实验的基础上,结合Sonar测量的基本特性,对静态结构化环境建模,并构造了广义基本信度赋值函数,利用经典DSm融合规则,融合每个栅格的声纳冗余信息,计算栅格占用的Bel.最后,以Pioneer Ⅱ 移动机器人作为试验平台,并在线对小型环境进行了3D栅格占用信度分布地图创建,其俯视图与实际2D地图中的物体外观轮廓及所在位置进行比较,其比较结果充分验证了算法的有效性,为进一步研究应用基于折扣理论的DSmT解决异类或同类非可靠多源信息融合,基于Hybrid DSmT的动态环境地图创建,以及多机器人联合创建地图和自定位奠定了坚实的基础.     

Conflict Reduction in Map Generalization Using Iterative Improvement

Map data are usually derived from a source that is based on a particular scale of representation and hence are subject to a particular degree of map generalization. Attempts to display data at scales smaller than the source can result in spatial conflict, whereby map symbols become too close or overlap. Several map generalization operators may be applied to resolve the problem, including displacement. In this paper we address the problem of displacing multiple map objects in order to resolve graphic conflict. Each of n objects is assigned k candidate positions into which it can possibly move, resulting in a total of kⁿ map realizations. The assumption is that some of these realizations will contain a reduced level of conflict. Generating and evaluating all realizations is however not practical, even for relatively small values of n and k. We present two iterative improvement algorithms, which limit the number of realizations processed. The first algorithm adopts a steepest gradient descent approach; the second uses simulated annealing. They are tested on a number of data sets and while both are successful in reducing conflict while limiting the number of realizations that are examined, the simulated annealing approach is superior with regard to the degree of conflict reduction. The approach adopted is regarded as generic, in the context of map generalization, in that it appears possible in principle to employ several map generalization operators combined with more sophisticated evaluation functions.     

Robust RBPF-SLAM for Indoor Mobile Robots Using Sonar Sensors in Non-Static Environments

We propose a robust simultaneous localization and mapping (SLAM) with a Rao-Blackwellized particle filter (RBPF) algorithm for mobile robots using sonar sensors in non-static environments. The algorithm consists of three parts: sampling from multiple ancestor sets, estimating intermediate paths for map updates and eliminating spurious landmarks using negative information from sonar sensors. The proposed sampling method, in which particles are sampled from multiple ancestor sets, increases the robustness of the estimation of the robot's pose, even if environmental changes corrupt observations. This step increases the probability of some particles being sampled from correct ancestor sets that are updated by observations reflected from stationary objects. When particles are sampled from several time steps earlier, however, observations at intermediate time steps cannot be used to update the map because of the lack of information about the intermediate path. To update the map with all sensor information, the intermediate path is estimated after particles are sampled from ancestor sets. Finally, spurious landmarks still exist on the map representing objects that were eliminated or that were extracted by error in cluttered areas. These are eliminated in the final step using negative information from the sonar sensors. The performance of the proposed SLAM algorithm was verified through simulations and experiments in various non-static environments.     

利用时差的单站无源相干定位最大似然算法

针对利用单个观测站接收多个第三方辐射源信号来估计目标位置的无源相干定位问题,提出了一种基于最大似然的时差定位算法。首先根据时差测量与目标位置之间的函数关系,构建目标位置的似然函数;然后采用牛顿迭代算法求解非线性似然函数的全局极大值,从而得到目标位置估计;推导了目标位置的最小二乘解,并将其作为牛顿迭代的初始解,从而保证算法的收敛性;最后还推导了算法的理论误差和克拉美罗界,并证明两者相等。仿真结果表明,算法的定位精度优于现有算法,且在测量误差适中时,定位精度可以达到克拉美罗界。通过几何精度因子图,分析了目标位置、外辐射源数量和位置等因素对定位精度的影响。     

Maximum Likelihood Estimation Using Parallel Computing: An Introduction to MPI

The computational difficulty of econometric problems has increased dramatically in recent years as econometricians examine more complicated models and utilize more sophisticated estimation techniques. Many problems in econometrics are `embarrassingly parallel' and can take advantage of parallel computing to reduce the wall clock time it takes to solve a problem. In this paper I demonstrate a method that can be used to solve a maximum likelihood problem using the MPI message passing library. The econometric problem is a simple multinomial logit model that does not require parallel computing but illustrates many of the problems one would confront when estimating more complicated models.     

基于RSSI差分似然估计的WSN节点定位算法

为了克服接收信号强度测量误差对无线传感嚣网络(WSN)节点自身定位精度的影响.在对极大似然估计定位算法和接收信号强度指示(RSSI)模型分析的基础上,定义了个体差异差分系数、距离差分系数和距离差分定位方程,把离目标节点最近的信标节点作为参考节点对基于RSSI的测距进行差分修正,并将测距差分修正和极大似然估计相结合提出了一种测距差分修正极大似然估计定位算法.算法通过RSSI进行测距,无需增加额外硬件开销,容易实现.定位精度可达2.5 m以下,适合于处理能力和能量有限的WSN节点定位.     

Track Compensation Algorithm Using Free Space Information with Occupancy Grid Map

Over the past few years, numerous technologies have emerged to enable safe and convenient driving. However, there still exist various problems autonomous vehicles should overcome. Precise detection and perception of surrounding environments are the essential foundations to overcome them. Consequently, many sensor fusion algorithms have been developed to handle more complex situations, with sensor manufacturers also making strenuous efforts to enhance sensor performance. Although Light Detection And Ranging(LiDAR) sensor generally outperforms other sensor types, they remain prohibitively expensive from car manufacturing companies perspective. Therefore, camera and radar sensors have been enhanced, and are starting to provide free space information, similar to LiDAR sensor data and somewhat different from target information they have previously provided. The aim of this paper was to utilize the free space information to improve track information for vehicles. We employ the probability model with two occupancy grid map (OGM) types, which are Bayesian theory and Dempster-Shafer theory based OGMs, to classify free space information states and to efficiently handle free space information. Final output from the proposed algorithm is the target vehicle’s compensated track. Experimental results verify superior performance compared with non-compensated algorithms.

     

用NE网格规整法实现线路平面图连续显示

基于对线路平面扫描图产生几何畸变的分析,提出了用NE网格规整方法对大幅面线路平面图进行网格分割与几何畸变校正,并按照NE网格进行图块的拼接,实现了带状平面图的连续无缝显示。     

A Hybrid Bayesian Framework for Map Matching: Formulation Using Switching Kalman Filter

This paper addresses an important issue for intelligent transportation system, namely the ability of vehicles to safely and reliably localize themselves within an a priori known road map network. For this purpose, we propose an approach based on hybrid dynamic bayesian networks enabling to implement in a unified framework two of the most successful families of probabilistic model commonly used for localization: linear Kalman filters and Hidden Markov Models. The combination of these two models enables to manage and manipulate multi-hypotheses and multi-modality of observations characterizing Map Matching problems and it improves integrity approach. Another contribution of the paper is a chained-form state space representation of vehicle evolution which permits to deal with non-linearity of the used odometry model. Experimental results, using data from encoders’ sensors, a DGPS receiver and an accurate digital roadmap, illustrate the performance of this approach, especially in ambiguous situations.     

基于人工免疫网络的连续过程传感器置信度评估

针对连续过程传感器置信度评估的问题,提出了一种基于人工免疫网络的在线数据处理算法。分析了Ishida动态识别免疫网络,在此基础上设计了模糊测试单元;使用模糊论域表达了动态识别免疫网络中抗体之间刺激的强度,建立了抗体浓度的数学模型,依照抗体的浓度来区分传感器的置信度;设计了模糊测试单元参数确定的方法,用以调整网络灵敏度和平衡的关系。算法应用于生物发酵过程传感器置信度评估,实验结果表明该算法能够对传感器的置信度进行有效评估、易于工程实现。     

Maximum Likelihood Least Squares Based Iterative Estimation for a Class of Bilinear Systems Using the Data Filtering Technique

International Journal of Control, Automation and Systems - Maximum likelihood methods are based on the probability and statistics theory, and significant for parameter estimation and system...     

Maximum Power Point Tracking With Fractional Order High Pass Filter for Proton Exchange Membrane Fuel Cell

Proton exchange membrane fuel cell (PEMFC) is widely recognized as a potentially renewable and green energy source based on hydrogen. Maximum power point tracking (MPPT) is one of the most important working conditions to be considered. In order to improve the performance such as convergence and robustness under disturbance and uncertainty, a fractional order high pass filter (FOHPF) is applied for the MPPT controller design based on the traditional extremum seeking control (ESC). The controller is designed with integerorder integrator (IO-I) and low pass filter (IO-LPF) together with fractional order high pass filter (FOHPF), by substituting the normal HPF in the original ESC system. With this FOHPF ESC, better convergence and smoother performance are achieved while maintaining the robust specifications. First, tracking stability is discussed under the commensurate-order condition. Then, simulation results are included to validate the proposed new FOHPF ESC scheme under disturbance. Finally, comparison results between FOHPF ESC and the traditional ESC method are also provided.      

基于集员滤波的双Kinect人体关节点数据融合

以Kinect为代表的深度图像传感器在肢体康复系统中得到广泛应用.单一深度图像传感器采集人体关节点数据时由于肢体遮挡、传感器数据错误和丢失等原因降低系统可靠性.本文研究了利用两台Kinect深度图像传感器进行数据融合从而达到消除遮挡、数据错误和丢失的目的,提高康复系统中数据的稳定性和可靠性.首先,利用两台Kinect采集患者健康侧手臂运动数据;其次,对两组数据做时间对准、Bursa线性模型下的坐标变换和基于集员滤波的数据融合;再次,将融合后的健康侧手臂运动数据经过“镜像运动”作为患侧手臂运动指令;最后,将患侧运动指令下发给可穿戴式镜像康复外骨骼带动患者患侧手臂完成三维动画提示的康复动作,达到患者主动可控康复的目的.本文通过Kinect与VICON系统联合实验以及7自由度机械臂控制实验验证了数据融合方法的有效性,以及两台Kinect可有效解决上述问题.     

Human Arm Motion Tracking by Inertial/Magnetic Sensors Using Unscented Kalman Filter and Relative Motion Constraint

Human motion tracking has many applications in biomedical and industrial services. Low-cost inertial/magnetic sensors are widely used in human motion capture systems to obtain the orientation of the human body segments. In this paper, we have presented a quaternion-based unscented Kalman filter algorithm to fuse inertial/magnetic sensors measurements for tracking human arm movements. In order to have a better estimation of the orientation of the forearm and the upper arm, a constraint equation was developed based on the relative velocity of the elbow joint with respect to the inertial sensors attached to the forearm and the upper arm. Also to compensate for fast body motions, we adapted the measurement covariance matrix in such a way that the filter implements gyroscopes when large accelerations are involved. The proposed algorithm was evaluated experimentally by an optical tracking system as the ground truth reference. The results showed the effectiveness and good performance of the proposed algorithm.     

Algorithm for Constructing Modular Projections for Correcting Multiple Errors Based on a Redundant Residue Number System Using Maximum Likelihood Decoding

Programming and Computer Software - One of the most important applications of the Redundant Residual Numbers System (RRNS) is to improve the fault tolerance of the data storage, processing, and...     

Improved Evaluation and Generation Of Grid Layouts Using Distance Preservation Quality and Linear Assignment Sorting

Images sorted by similarity enables more images to be viewed simultaneously, and can be very useful for stock photo agencies or e-commerce applications. Visually sorted grid layouts attempt to arrange images so that their proximity on the grid corresponds as closely as possible to their similarity. Various metrics exist for evaluating such arrangements, but there is low experimental evidence on correlation between human perceived quality and metric value. We propose distance preservation quality (DPQ) as a new metric to evaluate the quality of an arrangement. Extensive user testing revealed stronger correlation of DPQ with user-perceived quality and performance in image retrieval tasks compared to other metrics. In addition, we introduce Fast linear assignment sorting (FLAS) as a new algorithm for creating visually sorted grid layouts. FLAS achieves very good sorting qualities while improving run time and computational resources.