融合WiFi、激光雷达与地图的机器人室内定位

由于室内环境受多径效应影响,单一WiFi定位效果不佳;激光雷达(LiDAR)虽然测距定位精度高,但在室内存在大量单一、重复的场景结构(如走廊)时,往往会由于无法提取有效特征进行匹配而造成大量错误定位.因此,该文提出基于卡尔曼滤波框架的WiFi、激光雷达与地图的融合定位新方法.其中,滤波器的状态定义为机器人当前与历史时刻...     

传感器辅助的WiFi指纹室内定位方法

针对Wi Fi指纹室内定位技术中信号传播时变性大导致定位精度低的问题,提出一种传感器辅助的Wi Fi指纹定位方法。首先根据移动终端内置的加速度传感器判断用户的走步状态,以减少信号时变所引起的定位误差;然后利用终端中的磁力计与陀螺仪,通过传感器融合来计算用户的运动方向。最后结合所得的方向信息和历史位置计算终端位置,以减少指纹图中与终端反向的指纹带来的干扰,从而减少指纹匹配计算的复杂度。实验结果表明,提出的传感器辅助Wi Fi指纹定位技术能减小位置估计误差并提高定位的鲁棒性。     

基于激光雷达和特征地图的车辆智能定位研究

针对激光雷达采集行驶车辆的三维点云数据中包含过多畸变数据,影响车辆定位效果的问题,本文研究一种基于激光雷达和特征地图的车辆智能定位方法。激光雷达利用基于飞行时间的激光测距法,采集车辆及其行驶环境的三维激光点云数据,去除激光点云数据中的畸变数据。利用正态分布变换方法,优化删除畸变数据的点云集的正态分布概率值,配准三维激光点云数据。从完成配准后的三维激光点云数据中,提取柱状物体的圆形特征,构建车辆行驶的自然柱状特征地图。利用卡尔曼滤波算法,结合自然柱状特征地图信息,实现高精度的车辆智能定位。实验结果证明：该方法可以精准定位车辆目标,车辆智能定位精度较高,最高可达到97%,定位效率较好,最短可在5 s时间内完成定位,具有一定应用价值。     

一种抗干扰的室内WiFi指纹定位算法

随着现代网络技术和移动设备的飞速发展,高速便捷的WiFi已经深入人们生活的各种场合。人们除关注WiFi在网络服务质量方面的表现之外,也着眼于WiFi所能提供的位置服务。WiFi指纹定位是一种常用的定位方法,但实际定位中常有某个WiFi装置受到干扰的情况,如人员遮挡等。这将使WiFi指纹的某个特征可靠性降低,导致指纹匹配算法精度下降。针对此类问题,文章提出了一种称作最优特征分割的抗干扰指纹匹配方法,即找出最能代表这个指纹点的特征,并据此将指纹库迭代分割为子集,直至达到定位要求。最后通过仿真和实验验证了算法的可行性。     

智能移动机器人同步定位与地图构建算法研究

以移动机器人的同步定位与地图构建（SLAM）算法为研究对象,介绍了机器人同步定位与地图构建的原理,并对现有SLAM算法进行深入研究。对现有的SLAM算法进行改进,提出基于平方根UKF的SLAM算法,仿真结果表明,新算法达到提高SLAM算法的稳定性,减少算法运算复杂度并得到较高的估计精度的目的。     

基于动态环境的WiFi指纹自适应室内定位方法研究

通过WiFi进行室内定位早已不新鲜,然而其运行方法还存在一些问题,无法解决环境动态变化对RSSI值的影响,对此,相关工作者研究出了一种基于动态环境的WiFi指纹自适应室内定位方法,本文中通过相关资料和实验介绍了其研究历程、方法算法和作用,并且提出了一些改进意见,希望对稳定、准确的进行室内定位提供一些理论研究。     

自主移动机器人即时定位与地图构建方法研究

自主移动机器人的即时定位与地图构建是机器人领域中的关键问题。文中对自主移动机器人即时定位与地图构建研究的两个主要问题进行了分析,研究了不确定信息的描述和处理方法,以及数据关联的方法。综述了现阶段即时定位与地图构建的实现方法,其中包括两种卡尔曼滤波算法和粒子滤波算法,以及基于视觉的SLAM算法。通过分析现有算法的特点,提出了自主移动机器人即时定位与地图构建未来研究的发展方向。     

基于WiFi的分级室内定位

在经典的K最近邻(K-Nearest Neighbors,KNN)的WiFi定位方法中,其算法复杂度随着定位区域和定位区域内的WiFi接入点(Access Point,AP)的增加而增加,无法满足实时定位的要求.为此,提出一种分级WiFi定位算法.算法分为粗定位和精定位阶段,首先通过AP的可见性利用汉明距离寻找可能的子区域,再用KNN算法在子区域内(利用信号强度欧氏距离)进行精定位.经过实测数据验证,平均单次定位时间在KNN算法下下降了约95％,在最大后验算法下下降了约96％,表明所提分级定位框架具有延迟低的优点.     

嵌入式激光雷达下室内移动机器人动态定位方法

由于空间的限制,室内移动机器人在操作过程中对位置确认的要求较高,存在移动轨迹定位偏离的问题。为此提出嵌入式激光雷达下室内移动机器人动态定位方法。利用坐标转换以及欧式距离分割法对采集的激光雷达数据实施预处理,去除噪声对后续定位的影响。通过回波强度确定反光板的位置拟合变化,利用三边定位算法对移动机器人实施精准定位。经实验验证,该方法的定位误差较小,不仅可以实现室内移动机器人整体高精度的动态定位,而且可以很好地监测室内移动机器人的轨迹。     

无线WiFi与蓝牙Beacon融合定位

本文主要简述目前主流的室内定位技术及其面临的挑战,介绍无线WiFi与蓝牙Beacon融合定位的优势及其应用前景。     

Indoor Localization Scheme Using Magnetic Map for Smartphones

Wireless Personal Communications - The particle filter algorithm for geomagnetic matching has been combined with Pedestrian Dead-Reckoning (PDR) for indoor positioning, but the current particle...     

A WiFi Fingerprint Based High-Adaptability Indoor Localization via Machine Learning

For received signal strength(RSS)fingerprint based indoor localization approaches,the localization accuracy is significantly influenced by the RSS variance,device heterogeneity and environment complexity.In this work,we present a high-adaptability indoor localization(HAIL)approach,which leverages the advantages of both relative RSS values and absolute RSS values to achieve robustness and accuracy.Particularly,a backpropagation neural network(BPNN)is devised in HAIL to measure the fingerprints similarities based on absolute RSS values.With this aid,the characteristics of the applied area could be specially learned such that HAIL could be adaptive to different environments.The experiments demonstrate that HAIL achieves high localization accuracy with the average localization error of 0.87 m in the typical environments.Moreover,HAIL has the minimum amount of large errors and decreases the average localization error by about 30%~50%compared with the existing approaches.     

Indoor Localization Methods Using Dead Reckoning and 3D Map Matching

In order to navigate or localize in 3D space such as parking garages, we would need height information in addition to 2D position. Conventionally, an altimeter is used to get the floor level/height information. We propose a novel method for three-dimensional navigation and localization of a land vehicle in a multi-storey parking-garage. The solution presented in this paper uses low cost gyro and odometer sensors, combined with a 3D map by means of particle filtering and collision detection techniques to localize the vehicle in a parking garage. This eliminates the necessity of an altimeter or other additional aiding sources such as radio signalling. Altimeters have inherent dynamic influential factors such as temperature and environmental pressure affecting the altitude readings, and for radio signals we need extra infrastructure requirements. The proposed solution can be used without any such additional infrastructure devices. Other sources of information, such as WLAN signals, can be used to complement the solution if and when available. In addition we extend this proposed method to novel concept of non-stationary 3D maps, as moving maps, within which localization of a track-able object is required. We also introduce novel techniques that enable seamless navigation solution from vehicular dead reckoning (VDR) to pedestrian dead reckoning (PDR) and vice versa to reduce user involvement. For achieving this we collect relevant measurements such as vehicle ignition status and accelerometer signal variance, and user pattern recognition to select appropriate dead reckoning method.     

室内定位技术应用、研究现状及展望

通过对室内定位技术的典型应用场景进行介绍,归纳总结了现有室内定位技术的基本原理和实施方案,并对未来室内通信技术可能的研究热点进行了展望。     

基于单目相机与里程计的室内机器人定位研究

昂贵的定位传感器限制了室内服务机器人相关技术在实际生活中的发展.室内服务机器人定位技术的发展趋势之一是使用价格低廉的传感器.因此,基于单目相机与轮式里程计两种价格低廉的传感器,提出一种适用于室内动态环境的机器人定位解决方案.该方案将相机朝向天花板,以减少室内动态环境下信息多变带来的误差,改变特征匹配策略以提升重复纹理场...     

基于机器人激光定位的一种改进AMCL算法

高效的定位算法是实现机器人自主运动的前提,由于激光模型受复杂环境的限制,传统自适应蒙特卡罗定位(AMCL)算法提供的位姿精度有限.提出一种增加扫描匹配(SM)和离散傅里叶变换(DFT)的优化AMCL算法,将传统AMCL的加权均值输出作为SM的初始值,通过构建激光雷达观测点与先验地图的匹配函数模型,利用高斯牛顿的方法优化...     

Unsupervised Learning for Solving RSS Hardware Variance Problem in WiFi Localization

Hardware variance can significantly degrade the positional accuracy of RSS-based WiFi localization systems. Although manual adjustment can reduce positional error, this solution is not scalable as the number of new WiFi devices increases. We propose an unsupervised learning method to automatically solve the hardware variance problem in WiFi localization. This method was designed and implemented in a working WiFi positioning system and evaluated using different WiFi devices with diverse RSS signal patterns. Experimental results demonstrate that the proposed learning method improves positional accuracy within 100 s of learning time.     

基于WiFi的室内定位技术在博物馆的应用

在室内定位技术迅速发展的时代,由于WiFi技术的传输速度快、运行稳定、传输流量大等特点,因此在博物馆的展示导览服务领域内可以将WiFi技术与智能终端相结合,弥补传统人工讲解服务所存在的不足,既满足了观众需要,又有利于博物馆对展品的保护与管理。本文通过一个WiFi室内定位系统的实践应用展示了基于该技术在博物馆应用的可行性和有效性。     

Finding Near-Optimal Regularization Parameter for Indoor Device-free Localization

Device-free Localization (DfL) systems offer real-time indoor localization of people without any electronic devices attached on their bodies. The human body influences the radio wave propagation between wireless links and changes the Received Signal Strength (RSS). Wireless Sensor Networks (WSNs) nodes easily measure these RSS changes and appropriate Radio Tomographic Imaging (RTI) algorithms can then process the RSS data and allow human localization. This paper investigates how to choose near-optimal regularization parameter during the regularization process for indoor DfL and describes an experimental indoor DfL setup realized with a Sun SPOT based WSN. The work elaborates on the numerical calculation of the near-optimal regularization parameter by usage of the trade-off curve criterion. The calculated parameter enables conclusive RTI image with sufficient localization precision for eHealth or other ambient-assisted-living applications where the error tolerance is at a scale of several tens of centimeters. The value for the regularization parameter matches the empirical derived value obtained in the authors’previous work.     

Pedestrian Navigation Based on Inertial Sensors, Indoor Map, and WLAN Signals

As satellite signals, e.g. GPS, are severely degraded indoors or not available at all, other methods are needed for indoor positioning. In this paper, we propose methods for combining information from inertial sensors, indoor map, and WLAN signals for pedestrian indoor navigation. We present results of field tests where complementary extended Kalman filter was used to fuse together WLAN signal strengths and signals of an inertial sensor unit including one gyro and three-axis accelerometer. A particle filter was used to combine the inertial data with map information. The results show that both the map information and WLAN signals can be used to improve the pedestrian dead reckoning estimate based on inertial sensors. The results with different combinations of the available sensor information are compared.