Dynamic Modeling of a Class of Continuum Manipulators in Fixed Orientation

A novel simultaneous localization and mapping (SLAM) technique based on independent particle filters for landmark mapping and localization for a mobile robot based on a high-frequency (HF)-band radio-frequency identification (RFID) system is proposed in this paper. SLAM is a technique for performing self-localization and map building simultaneously. FastSLAM is a standard landmark-based SLAM method. RFID is a robust identification system with ID tags and readers over wireless communication; further, it is rarely affected by obstacles in the robot area or by lighting conditions. Therefore, RFID is useful for self-localization and mapping for a mobile robot with a reasonable accuracy and sufficient robustness. In this study, multiple HF-band RFID readers are embedded in the bottom of an omnidirectional vehicle, and a large number of tags are installed on the floor. The HF-band RFID tags are used as the landmarks of the environment. We found that FastSLAM is not appropriate for this condition for two reasons. First, the tag detection of the HF-band RFID system does not follow the standard Gaussian distribution, which FastSLAM is supposed to have. Second, FastSLAM does not have a sufficient scalability, which causes its failure to handle a large number of landmarks. Therefore, we propose a novel SLAM method with two independent particle filters to solve these problems. The first particle filter is for self-localization based on Monte Carlo localization. The second particle filter is for landmark mapping. The particle filters are nonparametric so that it can handle the non-Gaussian distribution of the landmark detection. The separation of localization and landmark mapping reduces the computational cost significantly. The proposed method is evaluated in simulated and real environments. The experimental results show that the proposed method has more precise localization and mapping and a lower computational cost than FastSLAM.     

Efficient data structures for model-based 3-D object recognitionand localization from range images

An effective method of surface characterization of 3D objects using surface curvature properties and an efficient approach to recognizing and localizing multiple 3D free-form objects (free-form object recognition and localization) are presented. The approach is surface based and is therefore not sensitive to noise and occlusion, forms hypothesis by local analysis of surface shapes, does not depend on the visibility of complete objects, and uses information from a CAD database in recognition and localization. A knowledge representation scheme for describing free-form surfaces is described. The data structure and procedures are well designed, so that the knowledge leads the system to intelligent behavior. Knowledge about surface shapes is abstracted from CAD models to direct the search in verification of vision hypotheses. The knowledge representation used eases processes of knowledge acquisition, information retrieval, modification of knowledge base, and reasoning for solution     

基于非度量多维标度的室内多标签协同定位算法

与有源标签相比,无源RFID标签成本较小,本文选取后者作为待定位标签。但是由于无源RFID标签之间无法通信,目前大多数传统的RFID定位算法一次只能定位一个标签而无法实现多标签同时定位。针对这一问题,提出了基于非度量多维标度(NMDS)的室内RFID多标签协同定位算法。利用到达相位差(PDOA)法拟合在多径存在环境下的测距误差,将待定位标签之间的距离差欧氏距离与非度量多维标度算法结合,计算出待定位标签的位置坐标。仿真结果表明,提出的算法可以通过一次非度量多维标度计算得到所有待定位标签的坐标,同时定位精度高于经典多维标度定位算法和传统三边定位算法。     

Localization of RFID-equipped assets during the operation phase of facilities

Indoor localization has gained importance as it has the potential to improve various processes related to the lifecycle management of facilities and to deliver personalized and location-based services (LBSs). Radio Frequency Identification (RFID) based systems, on the other hand, have been widely used in different applications in construction and maintenance. This paper investigates the usage of RFID technology for indoor localization of RFID equipped assets during the operation phase of facilities. The location-related data on RFID tags attached to assets are extracted from a Building Information Model (BIM) and can provide context-aware information inside the building which can improve Facilities Management (FM) processes. First, using the current location of the assets saved on the tags attached to fixed assets, an FM personnel is able to read tags from a distance and locate them on a floor plan. Fixed tags with known positions act as reference tags for RFID reader localization techniques. In this scenario, the user can also estimate his/her location by scanning the surrounding reference tags. Furthermore, the paper investigates an approach to locate moveable assets using received signals from available reference tags in the building based on pattern matching and clustering algorithms. As a result, a user equipped with an RFID reader is able to estimate his/her location, as well as the location of target assets, without having access to any Real-Time Location System (RTLS) infrastructure. Several case studies are used to demonstrate the feasibility of the proposed methods.     

Tag-only aging-counter localization for the R-LIM2 system

With the large number of items stored in present-day libraries, locating the exact position of an item is time-consuming. This is true not only for poorly managed libraries, but also for well maintained libraries. In the former case, the problem is rather serious. Even though an item may be somewhere in the library, the fundamental role of the library may not be fulfilled, if a user cannot locate the item. In the latter case, a user still needs to perform a tedious shelf search to locate the item. In the first version of the RFID-based Library Management (R-LIM) system, we attached expensive RFID antennas to every shelf, to acquire localization information about the items. However, the solution was not cost-effective, due to the large number of RFID antennas needed. Therefore, in this study, we propose tag-only aging-counter localization for R-LIM, which does not require shelf antennas to determine book locations. In this more recent version, viz., the R-LIM2 system, we replace expensive antennas with inexpensive RFID tags, which enables us to build an affordable system. By associating RFID tags attached to books with those attached to shelves, via periodic tag scans, we determine the exact location of books. This paper describes the aging-counter localization algorithm developed for R-LIM2, and explains how it can be integrated with other parts of the system to locate a book in a search.     

物联网中基于RFID的移动标签定位方案

当前基于RFID的定位系统无法使用异构移动阅读器按照分布式策略定位移动标签,且定位精度低下。针对这种情况,提出了LSMT-RFID系统。该系统使用一组异构移动式RFID阅读器来定位移动RFID标签,移动阅读器通过时间约束交错过程展开合作,附近的阅读器通过共享询问信息来相应地估计标签位置,并使用主动和被动协议来确保位置信息的及时传输。基于ns-3的仿真实验来评估方案的定位能力,重点考察了平均定位误差和定位延时。实验结果表明,本方案能实现有效定位。     

激光—相机系统语义栅格建图和路径规划

目的 SLAM（simultaneous localization and mapping）是移动机器人在未知环境进行探索、感知和导航的关键技术。激光SLAM测量精确，便于机器人导航和路径规划，但缺乏语义信息。而视觉SLAM的图像能提供丰富的语义信息，特征区分度更高，但其构建的地图不能直接用于路径规划和导航。为了实现移动机器人构建语义地图并在地图上进行路径规划，本文提出一种语义栅格建图方法。方法 建立可同步获取激光和语义数据的激光-相机系统，将采集的激光分割数据与目标检测算法获得的物体包围盒进行匹配，得到各物体对应的语义激光分割数据。将连续多帧语义激光分割数据同步融入占据栅格地图。对具有不同语义类别的栅格进行聚类，得到标注物体类别和轮廓的语义栅格地图。此外，针对语义栅格地图发布导航任务，利用路径搜索算法进行路径规划，并对其进行改进。结果 在实验室走廊和办公室分别进行了语义栅格建图的实验，并与原始栅格地图进行了比较。在语义栅格地图的基础上进行了路径规划，并采用了语义赋权算法对易移动物体的路径进行对比。结论 多种环境下的实验表明本文方法能获得与真实环境一致性较高、标注环境中物体类别和轮廓的语义栅格地图，且实验硬件结构简单、成本低、性能良好，适用于智能化机器人的导航和路径规划。     

一种新型红外立体视觉定位系统研究

为实现视觉目标可靠快速三维定位,设计了一种新型的基于DSP的嵌入式主动双目红外视觉系统。与普通的红外相机不同,该系统采用常规CCD相机加红外滤光片的新方法采集红外图像,通过红外滤光后的低噪声图像可以转化为低噪声二值化图像,以此提高目标定位的准确度。提出了离散化遍历视觉目标搜索算法,通过离散控制两个相机的旋转和倾斜运动,实现视觉目标快速主动遍历搜索。实验证明,该系统目标三维定位有良好的鲁棒性。     

一种拓扑约束的RFID三维定位方法*

针对现有RFID三维定位算法在运算时间复杂度问题上,收集的信息量较大、对定位的效率影响比较大,满足不了较高的RFID实时定位系统要求,提出了一种拓扑约束的RFID三维定位算法,使用虚拟地标与拓扑约束相结合对标签进行定位。通过引入一种松约束减少读写错误的影响,同时增加定位的精度。仿真结果表明,算法在不增加移动读写器时,定位精度为间隔的20%左右;在增加移动式读写器情况下,静态读写器间隔6m时,其瞬时定位精度为读写器间隔的12%,最终的迭代运算收敛后可获得0.4 m内的定位精度,为读写器间隔的6%。与传统LANDMARC系统定位精度为读写器间隔的50%左右相比,算法精度可以提高六倍以上。     

基于虚拟参考标签的RFID定位系统构建方法

针对现有RFID定位系统在位置服务单一、部署大量RFID设备相互间产生信号干扰而影响定位精度等方面的不足,利用经典信号传播模型在定位区域内构造虚拟参考标签空间,进而构建了一种基于虚拟参考标签空间的移动对象定位方法。实际测试表明,本系统具有较高的定位精度,可以有效克服部署大量参考标签及定位读写器引起的设备干扰问题,定位精度在中心区域达到0.31m~1. 65m,在外围区域误差为1. 48m~3.72m。     

Single tag sharing scheme for multiple-object RFID applications

RFID systems have been widely adopted in various industrial as well as personal applications. However, traditional RFID systems are limited to address only one tag for each application object. This limitation hinders the usability of RFID applications because it is difficult, if not impossible, to distinguish many tags simultaneously with existing RFID systems. In this paper, we propose a new RFID tag structure to support multiple-objects that can be easily shared by many different RFID applications. That is, the proposed RFID tag structure supports that a tag maintains several different objects and allows those applications to access them simultaneously. We also propose an authentication protocol to support multiple-object RFID applications. Especially, we focus on the efficiency of the authentication protocol by considering different security levels in RFID applications. The proposed protocol includes two types of authentication procedures. In the proposed protocol, an object has its security level and goes through one of different authentication procedures suitable for its security level. We report the results of a simulation to test the performance of the proposed scheme. In our simulation, we considered the safety of our scheme against potential attacks and evaluated the efficiency of the proposed protocol.     

Visual object retrieval via block-based visual-pattern matching

This paper presents an object-based image retrieval using a method based on visual-pattern matching. A visual pattern is obtained by detecting the line edge from a square block using the moment-preserving edge detector. It is desirable and yet remains as a challenge for querying multimedia data by finding an object inside a target image. Given an object model, an added difficulty is that the object might be translated, rotated, and scaled inside a target image. Object segmentation and recognition is the primary step of computer vision for applying to image retrieval of higher-level image analysis. However, automatic segmentation and recognition of objects via object models is a difficult task without a priori knowledge about the shape of objects. Instead of segmentation and detailed object representation, the objective of this research is to develop and apply computer vision methods that explore the structure of an image object by visual-pattern detection to retrieve images from a database. A voting scheme based on generalized Hough transform is proposed to provide object search method, which is invariant to the translation, rotation, scaling of image data, and hence, invariant to orientation and position. Computer simulation results show that the proposed method gives good performance in terms of retrieval accuracy and robustness.     

Robot Localization in Indoor Environments Using Radio Frequency Identification Technology and Stereo Vision

In this paper, a novel method of obstacle recognition and localization for mobile robots using radio frequency identification (RFID) technology and stereo vision is proposed. It is inexpensive, flexible and easy to use in practical environments. As information about the obstacles or environment can be written in ID tags, the proposed method can detect the obstacles easily and quickly compared with other methods. RF is not so stable, so the Bayes rule was introduced to calculate the probability where the ID tag exists in order to improve the accuracy of localizing obstacles with the ID tags. Then the stereo camera starts to process the image of the region of interest where the obstacle exists. The proposed method does not need to process all the image and easily gets some information about obstacles such as size, and color, and thus decreases the processing computation. Research on RFID technology integrating stereo vision to localize an indoor mobile robot has also been performed. This paper introduces the architecture of the proposed method and some experimental results.     

基于混沌加密的可同步更新RFID双向认证协议

针对日益突出的RFID系统安全隐私问题,提出了一个基于混沌序列的RFID双向认证协议。利用混沌对初始值的敏感性生成混沌序列,对密钥进行加密。该协议引入标签密钥动态更新机制,并设计了自同步解决方案,实现了对标签的二次认证。采用BAN逻辑对其安全性进行证明,并与已有的协议进行安全性分析和性能比较。其分析结果表明,该协议降低了标签成本,减少了标签和后端数据库的计算量,提高了后端数据库的检索效率。不仅有效地解决了RFID系统的隐私保护及安全问题,同时也提高了RFID协议认证的执行效率,更适合低成本的RF1D系统。     

Real-time tracking of multiple objects in space-variant vision based on magnocellular visual pathway

In this paper, we propose a space-variant image representation model based on properties of magnocellular visual pathway, which perform motion analysis, in human retina. Then, we present an algorithm for the tracking of multiple objects in the proposed space-variant model. The proposed space-variant model has two effective image representations for object recognition and motion analysis, respectively. Each image representation is based on properties of two types of ganglion cell, which are the beginning of two basic visual pathways; one is parvocellular and the other is magnocellular. Through this model, we can get the efficient data reduction capability with no great loss of important information. And, the proposed multiple objects tracking method is restricted in space-variant image. Typically, an object-tracking algorithm consists of several processes such as detection, prediction, matching, and updating. In particular, the matching process plays an important role in multiple objects tracking. In traditional vision, the matching process is simple when the target objects are rigid. In space-variant vision, however, it is very complicated although the target is rigid, because there may be deformation of an object region in the space-variant coordinate system when the target moves to another position. Therefore, we propose a deformation formula in order to solve the matching problem in space-variant vision. By solving this problem, we can efficiently implement multiple objects tracking in space-variant vision.     

Weakly supervised detection with decoupled attention-based deep representation

Training object detectors with only image-level annotations is an important problem with a variety of applications. However, due to the deformable nature of objects, a target object delineated by a bounding box always includes irrelevant context and occlusions, which causes large intra-class object variations and ambiguity in object-background distinction. For this reason, identifying the object of interest from a substantial amount of cluttered backgrounds is very challenging. In this paper, we propose a decoupled attention-based deep model to optimize region-based object representation. Different from existing approaches posing object representation in a single-tower model, our proposed network decouples object representation into two separate modules, i.e., image representation and attention localization. The image representation module captures content-based semantic representation, while the attention localization module regresses an attention map which simultaneously highlights the locations of the discriminative object parts and down weights the irrelevant backgrounds presented in the image. The combined representation alleviates the impact from the noisy context and occlusions inside an object bounding box. As a result, object-background ambiguity can be largely reduced and background regions can be suppressed effectively. In addition, the proposed object representation model can be seamlessly integrated into a state-of-the-art weakly supervised detection framework, and the entire model can be trained end-to-end. We extensively evaluate the detection performance on the PASCAL VOC 2007, VOC 2010 and VOC2012 datasets. Experimental results demonstrate that our approach effectively improves weakly supervised object detection.     

基于人工路标的易部署室内机器人全局定位系统

在一些布局易变或存在较多动态障碍物的室内,移动机器人的全局定位依然面临较大的应用挑战.针对这类场景,实现了一种新的基于人工路标的易部署室内机器人全局定位系统.该系统将人工路标粘贴在不易被遮挡的天花板上来作为参照物,仅依赖一个摄像头即能实现稳定的全局定位.整个系统根据具体的功能分为地图构建和全局定位两个过程.在地图构建过程中,系统使用激光SLAM算法所输出的位姿估计结果为基准,根据相机对路标点的观测信息来自动估计人工路标点在全局坐标系中的位姿,建立人工路标地图.而在全局定位过程中,该系统则是根据相机对地图中已知位姿的人工路标点的观测信息,结合里程计与IMU融合的预积分信息来对位姿进行实时估计.充分的实验测试表明,机器人在该系统所部署范围内运行的定位误差稳定在10 cm以内,且运行过程可以保证实时位姿输出,满足典型实际室内移动机器人全局定位的应用需求.     

多天线空分定位RFID阅读器的设计

RFID是近年来发展迅速的非接触式自动识别技术,为使基于RFID技术的智能管理系统具有定位识别的功能,本文提出了多天线空分定位RFID阅读器的设计方案,即采用阅读器与多个检测天线的分离式装置,使每个天线仅识别自己工作区域内的标签,天线的地址码与标签的数据共同构成传送的信息,从而使阅读器具有定位识别标签的功能。基于本文提出的RFID阅读器的智能战备药箱管理系统可以为战备药品提供安全、准确、实时的信息收集、处理和查询,该管理系统不仅可以提供定位识别,还可以节约系统的成本,具有广阔的应用前景。     

Ubiquitous Memories: a memory externalization system using physical objects

In this paper we propose an object-triggered human memory augmentation system named “Ubiquitous Memories” that enables a user to directly associate his/her experience data with physical objects by using a “touching” operation. A user conceptually encloses his/her experiences gathered through sense organs into physical objects by simply touching an object. The user can also disclose and re-experience for himself/herself the experiences accumulated in an object by the same operation. We implemented a prototype system composed basically of a radio frequency identification (RFID) device. Physical objects are also attached to RFID tags. We conducted two experiments. The first experiment confirms a succession of the “encoding specificity principle,” which is well known in the research field of psychology, to the Ubiquitous Memories system. The second experiment aims at a clarification of the system’s characteristics by comparing the system with other memory externalization strategies. The results show the Ubiquitous Memories system is effective for supporting memorization and recollection of contextual events.     

Improved visual SLAM: a novel approach to mapping and localization using visual landmarks in consecutive frames

Pathfinding is becoming more and more common in autonomous vehicle navigation, robot localization, and other computer vision applications. In this paper, a novel approach to mapping and localization is presented that extracts visual landmarks from a robot dataset acquired by a Kinect sensor. The visual landmarks are detected and recognized using the improved scale-invariant feature transform (I-SIFT) method. The methodology is based on detecting stable and invariant landmarks in consecutive (red-green-blue depth) RGB-D frames of the robot dataset. These landmarks are then used to determine the robot path, and a map is constructed by using the visual landmarks. A number of experiments were performed on various datasets in an indoor environment. The proposed method performs efficient landmark detection in various environments, which includes changes in rotation and illumination. The experimental results show that the proposed method can solve the simultaneous localization and mapping (SLAM) problem using stable visual landmarks, but with less computation time.