Relative Localization Method Research Articles

RF-SLAM: UHF-RFID Based Simultaneous Tags Mapping and Robot Localization Algorithm for Smart Warehouse Position Service

In this paper, we propose an RFID based simultaneous localization and mapping (RF-SLAM) method that allows us, for the first time, to estimate the robot's position and the tags' 3D position in the warehouse environment simultaneously without any reference tags and external sensors, using only COTS RFID device. RF-SLAM is designed to transform the RFID measurement into the relative tag position constraint and use a corresponding graph based model to solve the SLAM problem. Specifically, a multi-antenna based relative localization method using phase measurement and odometer data in a short time is proposed as the front end. The back end is a novel graph model based on the relative tags position constraint and odometer constraint. Experiments in different types of warehouses show the localization accuracy of robot and tags' 3D position is about 5cm and 10cm, respectively. The experimental results in a more challenging and actual environment are still competitive.

Effects of Stimulus Frequency and Location on Vibrotactile Discrimination Performance Using Voice Coil Actuators on the Forearm

What are the effects of frequency variation of vibrotactile stimuli on localization acuity? The precise localization of vibrotactile stimuli is crucial for applications that are aimed at conveying vibrotactile information. In order to evaluate the ability to distinguish between vibrotactile stimuli based on their frequency and location on the forearm, we used a relative point localization method. Participants were presented with pairs of sequential vibrotactile stimuli at three possible locations on the forearm and asked to determine whether the second stimulation occurred at the same location as the first one in the pair or not. The stimulation frequency varied between 100 Hz, 150 Hz, 200 Hz and 250 Hz, which covers the range of frequencies that human observers are most sensitive to. The amplitude was kept constant. Our results revealed that the ability to discriminate between actuators remained unaffected by variations in the frequency of vibrotactile stimulation within the tested frequency range. The accuracy of the tactile discrimination task was heavily dependent on the location of the stimulation on the forearm, with the highest accuracy close to the wrist and elbow, locations that may serve as tactile anchor points. Our results highlight the critical role of stimulation location in precise vibrotactile localization and the importance of careful consideration of location in the design of forearm-mounted vibrotactile devices.

Evaluating the Optimum Distance between Voice Coil Actuators Using the Relative Point Localization Method on the Forearm

While vibrotactile stimulation shows promise for sensory substitution devices, a crucial question concerns vibrotactile spatial resolution. We examined the optimum distance between three voice coil actuators (model: lofeltL5) on the forearm. Three actuators were embedded in a fabric-based vibrotactile sleeve where the actuators were placed in enclosures 3D-printed on the fabric. We used the relative point localization method where observers must discriminate whether two successive stimulations are in the same location or not. The resolution was measured for five vibrotactile sleeves, each with different distances between the actuators on the longitudinal axis of the forearm. The various distances were tested in a random order. In experiment one, pairs of stimuli were delivered sequentially in a random order to two adjacent actuators of the tactile sleeve on the upper side of the forearm. The task was to identify the perceived direction of the second stimulation (up, down, or the same) relative to the first one. Experiment two involved the same procedure but for the underside of the forearm. Taking the restrictions of the physical dimensions of the forearm and the design considerations into account, our results suggest that 20 mm is the optimum distance between the voice coil actuators (Model: Lofelt L5) for successful discrimination with high accuracy between the two stimulus locations on the forearm. There were no significant differences between the upper and undersides of the forearm.

Research on Android-odometry based on the estimation of GNSS temporal ambiguity variation with Xiaomi MI8

Following extant studies on temporal relative localization (TRL) methods, this study uses a doppler velocity constraint-based temporal ambiguity variation (TAV) detection method and a two-step TAV resolution method to obtain the high-precisiontemporal relative position variations over time based on raw GNSS observations of Android smartphones, commonly plagued by frequent half-cycle slips and lock-loss. Results show that the proportion of the detectable satellites over 0.65 reaches the 95.57% level and the fix rate of global TAV resolution reaches 82.93%. We then propose the Android-odometry method which is a real-time high-precision TRL method with time-differenced carrier-phase observations of Android smartphones. The experimental results show that the method achieves centimeter-level temporal relative position accuracy during the 13-min dynamic test with Xiaomi MI8, showing a dramatic improvement of over 90% compared to the traditional RTK method.

3-D Inter-Robot Relative Localization via Semidefinite Optimization

In this letter, the 3-D inter-robot relative localization problem is addressed using noise-corrupted odometric and distance measurements. Unlike the existing solutions, we are devoted to providing a relative localization method that has an “overall best performance,” which means that the tradeoffs between the estimation accuracy (EA), the number of measurements (NoMs), and the computation efficiency (CE) are considered. We demonstrate that an existing formulation of the 3-D relative localization problem, the square distances weighted least square (SD-WLS), can be equivalently reformulated as a non-convex quadratic constrained quadratic programming (QCQP) problem. Further, to handle the non-convex nature of the QCQP problem, we adopt the semidefinite programming (SDP) relaxation approach, which drops the rank constraint and recovers the solution of the QCQP via an eigenvalue decomposition strategy. Finally, a refinement step is introduced to solve the problem that the quadratic constraints might not be satisfied due to the SDP relaxation. The simulation and experiment results show that, compared to existing methods, our method has the best overall performance when the three factors, i.e., EA, NoMs, and CE, are important for a relative localization application.

A Survey of Robot Swarms' Relative Localization Method.

For robot swarm applications, accurate positioning is one of the most important requirements for avoiding collisions and keeping formations and cooperation between individuals. However, in some worst cases, the GNSS (Global Navigation Satellite System) signals are weak due to the crowd being in a swarm or blocked by a forest, mountains, and high buildings in the environment. Thus, relative localization is an indispensable way to provide position information for the swarm. In this paper, we review the status and development of relative localization. It is first assessed that relative localization to obtain spatio-temporal relationships between individuals is necessary to achieve the stable operation of the group. After analyzing typical relative localization systems and algorithms from the perspective of functionality and practicality, this paper concludes that the UWB-based (ultra wideband) system is suitable for the relative localization of robots in large-scale applications. Finally, after analyzing the current challenges in the field of fully distributed localization for robotic swarms, a complete mechanism encompassing the relative localization process and the relationship between local and global localization that can be a possible direction for future research is proposed.

Relative localization of swarm robotics based on the polar method

This article proposed a simple and efficient method for relative localization of swarm robotics. Firstly, the relative localization module of the micro-robot is designed to be composed of a compass and several pairs of infrared transceiver sensors evenly arranged on the edge of the robot. Based on the existing relative localization method and combined with the relative localization module, an improved relative localization method for swarm robots named as polar method is proposed, which includes distance calculation, relative direction calculation, and coordinates conversion. After that, a set of localization rules is designed to apply this method to the swarm system, which mainly include establishment of reference coordinate system, localization of the adjacent layer of the seed robot, and localization between layers, so that the initial coordinates of all robots can be obtained directly with only one seed robot. Then we described the implementation process of dynamic localization of swarm robots based on the initial static localization in the edge detour motion mode. Finally, the feasibility and flexibility of this localization method are verified by experiments on the swarm robotic platform, and the localization method is applied to realize the shape formation of swarm robots.

Relative Localization Method of Wet Spot of Grain using Array of Passive RFID Tags

Radio Frequency Identification (RFID) enables a large number of object monitoring since semi/passive tags are independent of batteries. In our previous work, the possibility of using different wireless technologies such as Wireless Sensor Network (WSN), Wireless Local Area Network (WLAN) and Radio Frequency Identification (RFID) to determine the moisture content in rice was investigated. Finding from our previous work suggest that RFID can be used to determine the moisture content of rice. While numerous research have been conducted for moisture content of grain, however, to author’s knowledge, there is only a few studies conducted on the localization of grain hostpot. Therefore, this study aims to investigate if the passive RFID array can be used to localize the location of the wet spot of grain. Prior, the experiment, a suitable setting for the RFID system were determined. In addition, a simple test was conducted to select a suitable operating frequency. From the investigation, the result indicates that only frequency channels 865, 866, 867, 868 and 869 MHz can detect all 30 tags. Meanwhile, frequency channel in the range 902 to 928 MHz detects 26 to 29 unique tags. Hence, 868 MHz was selected as the operating frequency throughout the experiment. The findings indicate that the RSSI value measured by the RFID reader decreased as the moisture of the sample increased when the tags were blocked by the sample placed at the designated location during the test.

ReLoc 2.0: UHF-RFID Relative Localization for Drone-Based Inventory Management

Radio frequency identification (RFID) technology attracts extensive attention for industrial applications, especially supply chain businesses and inventory management. In this article, a passive RFID localization scheme based on unmanned aerial vehicles (UAVs) or drones is established for inventory management in warehouses. In case of the major challenge of the on-board antenna tracking errors in practical scenarios, the state-of-the-art (SOTA) numerical methods rely on accurate antenna positions, such as synthetic aperture radar (SAR)-based algorithms, which will introduce large positioning errors and become unreliable. To this end, we propose a new relative RFID localization method based on phase, received signal strength indicator (RSSI), and readability, which is little affected by antenna tracking errors. In the proposed method, the tagged items on the racks are located laterally through pinpointing the minimum of the unwrapped phases, which have been unwrapped based on the hybrid random forest (RF) model. RSSI differences and the read count of each patch antenna’s beam are utilized to distinguish the rack level of tagged assets via a boosting tree classifier. The experimental results in a real warehouse show that the proposed method achieves 27.1-cm mean lateral positioning accuracy and orders the tagged items horizontally and vertically with more than 96.7% and 98.0% accuracy, respectively.

A Comparative Study of RSSI-Based Localization Methods: RSSI Variation Caused by Human Presence and Movement

In a received signal strength indicator (RSSI) based localization system, the presence or movement of humans is one of the major effects causing RSSI variation. Using RSSI data during such a situation to estimate the target position can give large errors. Regarding this problem, in this paper, a comparison of several RSSI-based localization methods with and without human presence and movement were investigated experimentally. The major contribution of this work is that the well-known and widely used RSSI-based localization methods presented in the literature, including the min–max, the trilateration, the weighted centroid localization (WCL), and the relative span exponential weighted localization (REWL) methods, were tested. Thus, how human presence or absence influences the accuracy of these methods, and which methods show the best estimates while tolerating human movement effects can be investigated. The experiments were carried out in a laboratory and in a parking building. The results demonstrate that, without human movement effects, all methods perform very similarly. In contrast, human movements significantly increased estimation errors.Here, the maximum distance errors of the min–max, the trilateration, the WCL, and the REWL are 1.34 m, 4.09 m, 1.25 m, and 1.24 m, respectively. Obviously, the min–max, the WCL (with an optimal parameter), and the REWL (with the optimal parameter) can well tolerate the RSSI variations caused by human movements and provide significantly better accuracy than the trilateration method. Based on these findings, all the mentioned localization methods should be further improved to deal with the human movement problem.

Global Spatial and Local Spectral Similarity-Based Manifold Learning Group Sparse Representation for Hyperspectral Imagery Classification

Spectral–spatial framework has been widely applied for hyperspectral image classification task. Some well-established models, such as group sparse representation (GSR), have gained a certain advance but still mainly focus on the usage of local spatial similarity and neglect the nonlocal spatial information. Recently, nonlocal self-similarity (NLSS) has been exploited to support the spatial coherence tasks. However, current NLSS-based methods are biased toward the direct use of nonlocal spatial information as a whole, while the underlying spectral information is not well exploited. In this article, we proposed a novel method to exploit local spectral similarity through nonlocal spatial similarity, with the integration of local spatial consistency in a single framework. Specifically, the proposed approach first exploits the NLSS by searching the nonoverlapped similar patches in defined scopes. Then, spectral similarity is determined locally within the found patches. After that, the found similar data and the original data are fused in a designed pattern. Finally, the GSR-based classifier (GSRC) is applied to process the fused data characterized by the manifold learning algorithm. The experimental results based on three real hyperspectral data sets demonstrate the efficiency of the proposed method, with improvements over the other related nonlocal or local similarity-based methods.

Marker-Less Micro Aerial Vehicle Detection and Localization Using Convolutional Neural Networks

A relative localization system for micro aerial vehicles (MAVs), which is able to work without any markers or other specialized equipment, is presented in this letter. The system utilizes images from an onboard camera to detect nearby MAVs using a convolutional neural network. When compared to traditional computer vision-based relative localization systems, this approach removes the need for specialized markers to be placed on the MAVs, saving weight and space, while also enabling localization of non-cooperating robots. The system is designed and implemented to run online, onboard an MAV platform in order to enable relative stabilization of several MAVs in a formation or swarm-like behavior, when operating in a closed feedback loop with the control system of the MAVs. We demonstrate the viability and robustness of the proposed method in real-world experiments. The method was also designed for the purpose of autonomous aerial interception and is a fitting complement to other MAV detection and relative localization methods for this purpose, as is shown in the experiments.

Enhanced Relative Localization Based on Persistent Excitation for Multi-UAVs in GPS-Denied Environments

Intelligent unmanned aerial vehicles (UAVs) have been applied for civil and military uses. Relative localization (RL) is crucial for multi-UAVs to accomplish complex tasks successfully and safely. In global positioning system (GPS) denied environments, where accurate or meaningful location information is hard to obtain, persistent excitation based RL is a promising approach for multi-UAVs to achieve RL without any needs of external infrastructures. However, for many cases, existing persistent excitation based RL method suffers precision loss, error accumulation and divergence. This article tackles these issues, and proposes an enhanced approach to ensure the practical usage of persistent excitation based RL. Synchronized sensor sample prediction is introduced to confine and reduce RL error, and RL estimation is redesigned to avoid RL error accumulation. We evaluated our solution by simulating various scenarios. The results show that the proposed approach can effectively decrease RL error and prevent RL error accumulation.

Unique 4-DOF Relative Pose Estimation with Six Distances for UWB/V-SLAM-Based Devices.

In this work we introduce a relative localization method that estimates the coordinate frame transformation between two devices based on distance measurements. We present a linear algorithm that calculates the relative pose in 2D or 3D with four degrees of freedom (4-DOF). This algorithm needs a minimum of five or six distance measurements, respectively, to estimate the relative pose uniquely. We use the linear algorithm in conjunction with outlier detection algorithms and as a good initial estimate for iterative least squares refinement. The proposed method outperforms other related linear methods in terms of distance measurements needed and in terms of accuracy. In comparison with a related linear algorithm in 2D, we can reduce 10% of the translation error. In contrast to the more general 6-DOF linear algorithm, our 4-DOF method reduces the minimum distances needed from ten to six and the rotation error by a factor of four at the standard deviation of our ultra-wideband (UWB) transponders. When using the same amount of measurements the orientation error and translation error are approximately reduced to a factor of ten. We validate our method with simulations and an experimental setup, where we integrate ultra-wideband (UWB) technology into simultaneous localization and mapping (SLAM)-based devices. The presented relative pose estimation method is intended for use in augmented reality applications for cooperative localization with head-mounted displays. We foresee practical use cases of this method in cooperative SLAM, where map merging is performed in the most proactive manner.

Structured graph regularized shape prior and cross-entropy induced active contour model for myocardium segmentation in CTA images

Accurate segmentation of myocardium tissue from computed tomography angiography (CTA) images is a crucial step in development of cardiac clinical applications. Automatic methods are highly desirable to release the burden of manual delineation, however, it is challenging due to the presence of images with intensity non-uniformity and coarse, weak and even pseudo boundary. To this end, a new geometric active contour (GAC) model that integrates high-level shape prior and low-level local intensity statistics is proposed. First, the cardiac-type specific shape prior is learned by structured graph-regularized principal component analysis, so that allows to be faithful to the shape of the desired myocardium. Second, local intensity distribution with inhomogeneity is modeled by cross-entropy energy functional and segmentation-oriented image decomposition. Third, the proposed model is solved as variational level set formulation and a distance regularized energy functional is also incorporated for the stability of numerical computation. The model was evaluated on a set of cardiac CTA images with comparison to related shape prior and local region-based methods and multi-atlas joint label fusion methods, and experimental results show it achieves competitive accuracies of segmenting myocardial epicardium and endocardium parts. It also presents advantage of computational burden in comparison to some popular methods such as multi-atlas joint label fusion. Since the framework of proposed method is general and adaptive, it could be potentially extended to segment similar objects in CT images.

UVDAR System for Visual Relative Localization With Application to Leader–Follower Formations of Multirotor UAVs

A novel onboard relative localization method, based on ultraviolet light, used for real-time control of a leader–follower formation of multirotor UAVs, is presented in this paper. A new smart sensor, UVDAR, is employed in an innovative way, which does not require communication and is extremely reliable in real-world conditions. This innovative sensing system exploits UV spectrum and provides relative position and yaw measurements independently of environment conditions such as changing illumination and the presence of undesirable light sources and their reflections. The proposed approach exploits this retrieved information to steer the follower to a given three-dimensional position and orientation relative to the leader, which may be considered as the main building block of any multi-UAV system operating with small mutual distances among team members. The proposed solution was verified in demanding outdoor conditions, validating the usage of UVDAR in real flight scenario, and paving the way for further usage of UVDAR for practical multi-UAV formation deployments.

Some Developments and New Insights for Microseismic/Acoustic Emission Source Localization

The purpose of this review is to discuss the development of the methods in microseismic/acoustic emission (AE) source localization and detail the principles of the mainly developed methods. The important applications of the microseismic/AE in engineering practice and the history of the source localization (from the initial Geiger method of localization to the later double‐difference method) are introduced briefly in the beginning of the review. The factors that influence the accuracy of source locating are discussed in Section 2 to provide references on how to improve the accuracy of the localization methods. Then, the main localization methods in the history of the source and AE research have been classified into four parts (i.e., the localization methods for single event, the joint inversion methods, the relative localization methods, and the localization methods in spatial domain); each part is extended and systematically reviewed, respectively, from Sections 3 to 6, where the merit and demerit of the methods are discussed. Finally, a brief summary and some aspects of prospective research are presented.

PRDL: Relative Localization Method of RFID Tags via Phase and RSSI Based on Deep Learning

Ultra-high frequency radio frequency identification (UHF RFID) technology has been widely used in many areas, and RFID localization becomes a research hotspot. There are many kinds of research on absolute localization; however, due to some disadvantages of absolute localization, relative localization is more effective in some situations. At present, there are some problems with relative localization: existing methods have low localization accuracy, and it is difficult for them to deal with high-density tags. Aiming at these problems, this paper proposes PRDL: relative localization method of RFID tags via phase and RSSI based on deep learning. By using deep learning, the variation characteristics of RFID phase and RSSI are extracted with limited data accuracy conditions. On this basis, we can infer the relative positional relationship of RFID tags with high accuracy, and design the corresponding sorting algorithm to obtain the sequence arrangement. PRDL has experimented with bare tags and actual books, and the experimental results show that PRDL can achieve better results than the traditional relative localization methods. A series of tests also showed that PRDL has good robustness and generalization ability.

Heading Estimation for Pedestrian Dead Reckoning Based on Robust Adaptive Kalman Filtering.

Pedestrian dead reckoning (PDR) using smart phone-embedded micro-electro-mechanical system (MEMS) sensors plays a key role in ubiquitous localization indoors and outdoors. However, as a relative localization method, it suffers from the problem of error accumulation which prevents it from long term independent running. Heading estimation error is one of the main location error sources, and therefore, in order to improve the location tracking performance of the PDR method in complex environments, an approach based on robust adaptive Kalman filtering (RAKF) for estimating accurate headings is proposed. In our approach, outputs from gyroscope, accelerometer, and magnetometer sensors are fused using the solution of Kalman filtering (KF) that the heading measurements derived from accelerations and magnetic field data are used to correct the states integrated from angular rates. In order to identify and control measurement outliers, a maximum likelihood-type estimator (M-estimator)-based model is used. Moreover, an adaptive factor is applied to resist the negative effects of state model disturbances. Extensive experiments under static and dynamic conditions were conducted in indoor environments. The experimental results demonstrate the proposed approach provides more accurate heading estimates and supports more robust and dynamic adaptive location tracking, compared with methods based on conventional KF.

On-board communication-based relative localization for collision avoidance in Micro Air Vehicle teams

To avoid collisions, Micro Air Vehicles (MAVs) flying in teams require estimates of their relative locations, preferably with minimal mass and processing burden. We present a relative localization method where MAVs need only to communicate with each other using their wireless transceiver. The MAVs exchange on-board states (velocity, height, orientation) while the signal strength indicates range. Fusing these quantities provides a relative location estimate. We used this for collision avoidance in tight areas, testing with up to three AR.Drones in a 4,mathrm{m}~mathbf {times }~4,mathrm{m} area and with two miniature drones (approx 50,mathrm{g}) in a 2~mathrm{m}~mathbf {times }~2~mathrm{m} area. The MAVs could localize each other and fly several minutes without collisions. In our implementation, MAVs communicated using Bluetooth antennas. The results were robust to the high noise and disturbances in signal strength. They could improve further by using transceivers with more accurate signal strength readings.