Collaborative Localization Research Articles

Detection of UWB ranging measurement quality for collaborative indoor positioning

Wireless communication signals have become popular alternatives for indoor positioning and navigation due to lack of navigation satellite signals in such environments. The signal characteristics determine the method used for positioning as well as the positioning accuracy. Ultra-wideband (UWB) signals, with a typical bandwidth of over 1 GHz, overcome multipath problems in complicated environments. Hence, potentially achieves centimetre-level ranging accuracy in open areas. However, signals can be disrupted when placed in environments with obstructions and cause large ranging errors. This paper proposes a ranging measurement quality indicator (RQI) which detects the UWB measurement quality based on the received signal strength pattern. With a detection validity of more than 83%, the RQI is then implemented in a ranging-based collaborative positioning system. The relative constraint of the collaborative network is adjusted adaptively according to the detected RQI. The proposed detection and positioning algorithm improves positioning accuracy by 80% compared to non-adaptive collaborative positioning.

The Video Collaborative Localization of a Miner's Lamp Based on Wireless Multimedia Sensor Networks for Underground Coal Mines.

Based on wireless multimedia sensor networks (WMSNs) deployed in an underground coal mine, a miner’s lamp video collaborative localization algorithm was proposed to locate miners in the scene of insufficient illumination and bifurcated structures of underground tunnels. In bifurcation area, several camera nodes are deployed along the longitudinal direction of tunnels, forming a collaborative cluster in wireless way to monitor and locate miners in underground tunnels. Cap-lamps are regarded as the feature of miners in the scene of insufficient illumination of underground tunnels, which means that miners can be identified by detecting their cap-lamps. A miner’s lamp will project mapping points on the imaging plane of collaborative cameras and the coordinates of mapping points are calculated by collaborative cameras. Then, multiple straight lines between the positions of collaborative cameras and their corresponding mapping points are established. To find the three-dimension (3D) coordinate location of the miner’s lamp a least square method is proposed to get the optimal intersection of the multiple straight lines. Tests were carried out both in a corridor and a realistic scenario of underground tunnel, which show that the proposed miner’s lamp video collaborative localization algorithm has good effectiveness, robustness and localization accuracy in real world conditions of underground tunnels.

An Adaptive Weighting based on Modified DOP for Collaborative Indoor Positioning

Indoor localisation has always been a challenging problem due to poor Global Navigation Satellite System (GNSS) availability in such environments. While inertial measurement sensors have become popular solutions for indoor positioning, they suffer large drifts after initialisation. Collaborative positioning enhances positioning robustness by integrating multiple localisation information, especially relative ranging measurements between local users and transmitters. However, not all ranging measurements are useful throughout the whole positioning process and integrating too much data will increase the computation cost. To enable a more reliable positioning system, an adaptive collaborative positioning algorithm is proposed which selects units for the collaborative network and integrates ranging measurement to constrain inertial measurement errors. The algorithm selects the network adaptively from three perspectives: the network geometry, the network size and the accuracy level of the ranging measurements between the units. The collaborative relative constraint is then defined according to the selected network geometry and anticipated measurement quality. In the case of trials with real data, the positioning accuracy is improved by 60% by adjusting the range constraint adaptively according to the selected network situation, while also improving the system robustness.

TDOA-Based Source Collaborative Localization via Semidefinite Relaxation in Sensor Networks

The time delay of arrival- (TDOA-) based source localization using a wireless sensor network has been considered in this paper. The maximum likelihood estimate (MLE) is formulated by taking the correlated TDOA noise into account, which is caused by the difference with the TOA of the reference sensor. The global optimal solution is difficult to obtain due to the nonconvex nature of the ML function. We propose an alternative semidefinite programming method, which transforms the original ML problem into a convex one by relaxing nonconvex equalities into convex matrix inequalities. In addition, the source localization algorithm in the presence of sensor location errors and non-line-of-sight (NLOS) observations is developed. Our simulation results demonstrate the potential advantages of the proposed method. Furthermore, the proposed source localization algorithm by taking the NLOS TOA measurements as the constraints of the convex problem can provide a good estimate.

Collaborative Localization and Location Verification in WSNs.

Localization is one of the most important technologies in wireless sensor networks. A lightweight distributed node localization scheme is proposed by considering the limited computational capacity of WSNs. The proposed scheme introduces the virtual force model to determine the location by incremental refinement. Aiming at solving the drifting problem and malicious anchor problem, a location verification algorithm based on the virtual force mode is presented. In addition, an anchor promotion algorithm using the localization reliability model is proposed to re-locate the drifted nodes. Extended simulation experiments indicate that the localization algorithm has relatively high precision and the location verification algorithm has relatively high accuracy. The communication overhead of these algorithms is relative low, and the whole set of reliable localization methods is practical as well as comprehensive.

Collaborative Positioning in Ad Hoc Networks Using Passive Time- Differences of Arrival

Accuracy and scalability contribute to the quality of location solutions in wireless networks. While accuracy is a parameter addressed in all location technologies, scalability is often relegated. However, scalability becomes an actual issue in positioning solutions based message exchange (e.g. measuring ranges from beacons). The passive TDOA algorithm is a solution designed to localize terminals in a mobile ad hoc network in a passive way. It is achieved by computing time-differences of arrival from messages being exchanged by other network nodes (i.e. the active nodes), but just listening to the radio channel. In advanced stages of the passive TDOA algorithm (i.e. autonomous passive TDOA recently patented), it is able to compute the position of the unknown node (i.e. the passive node) and of the nodes which the listened location messages belong to (i.e. the active nodes). Thus, the requirement of active nodes informing about its own position to passive nodes is overcome. From the network perspective, the impact of this joint-position computation: it boosts the scalability since less information is transmitted and it helps to improve the accuracy of the active nodes, because passive nodes can report several positions regarding a single active node. However, the accuracy of the positions computed by means of the passive TDOA algorithm needs to be good enough to attend the user’s demands. The aim of this work is analyzing the accuracy achieved by the passive TDOA, focusing on the loss of precision due to passivity if compared with conventional two-way time-of-arrival active solutions, which passive TDOA is rely on.

Collaborative Navigation as a Solution for PNT Applications in GNSS Challenged Environments – Report on Field Trials of a Joint FIG / IAG Working Group

AbstractPNT stands for Positioning, Navigation, and Timing. Space-based PNT refers to the capabilities enabled by GNSS, and enhanced by Ground and Space-based Augmentation Systems (GBAS and SBAS), which provide position, velocity, and timing information to an unlimited number of users around the world, allowing every user to operate in the same reference system and timing standard. Such information has become increasingly critical to the security, safety, prosperity, and overall qualityof-life of many citizens. As a result, space-based PNT is now widely recognized as an essential element of the global information infrastructure. This paper discusses the importance of the availability and continuity of PNT information, whose application, scope and significance have exploded in the past 10–15 years. A paradigm shift in the navigation solution has been observed in recent years. It has been manifested by an evolution from traditional single sensor-based solutions, to multiple sensor-based solutions and ultimately to collaborative navigation and layered sensing, using non-traditional sensors and techniques – so called signals of opportunity. A joint working group under the auspices of the International Federation of Surveyors (FIG) and the International Association of Geodesy (IAG), entitled ‘Ubiquitous Positioning Systems’ investigated the use of Collaborative Positioning (CP) through several field trials over the past four years. In this paper, the concept of CP is discussed in detail and selected results of these experiments are presented. It is demonstrated here, that CP is a viable solution if a ‘network’ or ‘neighbourhood’ of users is to be positioned / navigated together, as it increases the accuracy, integrity, availability, and continuity of the PNT information for all users.

Appearance-Based Place Recognition Using Whole-Image BRISK for Collaborative Multi-Robot Localization

Appearance-Based Place Recognition Using Whole-Image BRISK for Collaborative Multi-Robot Localization

MANCL: a multi‐anchor nodes collaborative localization algorithm for underwater acoustic sensor networks

AbstractLocalization is an essential and major issue for underwater acoustic sensor networks (UASNs). Almost all the applications in UASNs are closely related to the locations of sensors. In this paper, we propose a multi‐anchor nodes collaborative localization (MANCL) algorithm, a three‐dimensional (3D) localization scheme using anchor nodes and upgrade anchor nodes within two hops for UASNs. The MANCL algorithm divides the whole localization process into four sub‐processes: unknown node localization process, iterative location estimation process, improved 3D Euclidean distance estimation process, and 3D DV‐hop distance estimation process based on two‐hop anchor nodes. In the third sub‐process, we propose a communication mechanism and a vote mechanism to determine the temporary coordinates of unknown nodes. In the fourth sub‐process, we use two‐hop anchor nodes to help localize unknown nodes. We also evaluate and compare the proposed algorithm with a large‐scale localization algorithm through simulations. Results show that the proposed MANCL algorithm can perform better with regard to localization ratio, average localization error, and energy consumption in UASNs. Copyright © 2014 John Wiley & Sons, Ltd.

Analytical and simulation models for collaborative localization

Collaborative localization is a special case for knowledge fusion where information is exchanged in order to attain improved global and local knowledge. We propose analytical as well as agent based simulation models for pedestrian dead reckoning (PDR) systems in agents collaborating to improve their location estimate by exchanging subjective position information when two agents are detected close to each other. The basis of improvement is the fact that two agents are at approximately the same position when they meet, and this can be used to update local position information. In analytical models we find that the localization error remains asymptotically finite in infinite systems or when there is at least one immobile agent (i.e. an agent with a zero localization error) in the system. In the agent model we tested finite systems under realistic (that is, inexact) meeting conditions and tested localization errors as function of several parameters. We found that a large finite system comprising hundreds of users is capable of collaborative localization with an essentially constant error under various conditions. The presented models can be used for predicting the improvement in localization that can be achieved by a collaboration among several mobile computers. Besides, our results can be considered as first steps toward a more general collaborative (incremental) form of knowledge fusion.

Meditative Dialogue: Cultivating the Transformative Theater of Psychotherapy

This article offers a selective review of literature on the use of improvisation and play to promote “the bursting forth from the unknown in the moment” (Kindler, 2010, p. 224) in what I term the “theater of psychotherapy.” It presents an innovative Meditative Dialogue process through which clients and their therapists are able to cultivate and access this “theater” as they co-create creative spaces in which transformative experiences are accessible. A brief vignette offers an illustration of how the Meditative Dialogue process helps to develop intimacy, presence, and focus through a collaborative positioning of curiosity, openness, and enlivenment in the therapeutic relationship.

Relative observation for multi-robot collaborative localisation based on multi-source signals

This paper describes a synthesising method for multi-robot collaborative localisation. A distributed extended Kalman filter (EKF) based on robot odometry and external North Star signals for data fusion is first designed for the localisation of individuals in the robot group. Relying on relative observation by infrared sensors and gyroscopes mounted on robots, and the ‘uncertainty volume’ strategy, the positions estimated by EKFs are further corrected for precising the localisation process. The localisation accuracy based on different sensing regimes is tested. Sensor correlations and uncertainties are analysed for predicting error propagation and to accommodate sensing deviations. The multi-source signals are then synthesised for the collaborative localisation for a multi-robot system without introducing excessive computation. Finally, this work is verified by both simulation and experiments with real robots, i.e. the Festo Robotinos under different scenarios.

Distributed collaborative 3D pose estimation of robots from heterogeneous relative measurements: an optimization on manifold approach

SUMMARYWe propose a distributed algorithm for estimating the 3D pose (position and orientation) of multiple robots with respect to a common frame of reference when Global Positioning System is not available. This algorithm does not rely on the use of any maps, or the ability to recognize landmarks in the environment. Instead, we assume that noisy relative measurements between pairs of robots are intermittently available, which can be any one, or combination, of the following: relative pose, relative orientation, relative position, relative bearing, and relative distance. The additional information about each robot's pose provided by these measurements are used to improve over self-localization estimates. The proposed method is similar to a pose-graph optimization algorithm in spirit: pose estimates are obtained by solving an optimization problem in the underlying Riemannian manifold $(SO(3)\times{\mathcal R}^3)^{n(k)}$. The proposed algorithm is directly applicable to 3D pose estimation, can fuse heterogeneous measurement types, and can handle arbitrary time variation in the neighbor relationships among robots. Simulations show that the errors in the pose estimates obtained using this algorithm are significantly lower than what is achieved when robots estimate their pose without cooperation. Results from experiments with a pair of ground robots with vision-based sensors reinforce these findings. Further, simulations comparing the proposed algorithm with two state-of-the-art existing collaborative localization algorithms identify under what circumstances the proposed algorithm performs better than the existing methods. In addition, the question of trade-offs between cost (of obtaining a certain type of relative measurement) and benefit (improvement in localization accuracy) for various types of relative measurements is considered.

Collaborative localization in visual sensor networks

Collaboration in visual sensor networks is essential not only to compensate for the limitations of each sensor node but also to tolerate inaccurate information generated by faulty sensors. This article focuses on the design of a collaborative target localization algorithm that is resilient to sensor faults. We first develop a distributed solution to fault-tolerant target localization based on a so-called certainty map . To tolerate potential sensor faults, a voting mechanism is adopted and a threshold value needs to be specified which is the key to the realization of the distributed solution. Analytical study is conducted to derive the lower and upper bounds for the threshold such that the probability of faulty sensors negatively impacts the localization performance is less than a small value. Second, we focus on the detection and correction of one type of sensor faults, error in camera orientation. We construct a generative image model in each camera based on the detected target location to estimate camera's orientation, detect inaccuracies in camera orientations and correct them before they cascade. Based on results obtained from both simulation and real experiments, we show that the proposed method is effective in localization accuracy as well as fault detection and correction performance.

EMP: Exploiting Mobility Patterns for Collaborative Localization in Sparse Mobile Networks

Location awareness plays an indispensable role in a wide variety of application domains such as environment monitoring and vehicle tracking. In this paper we focus on the localization of mobile users in sparse mobile networks which exist in many practical scenarios where users are distributed over a vast area. The unique characteristics of sparse mobile networks present several challenges for accurate localization, such as constant movement and little information from anchors. By analyzing five large datasets of real users traces with entropy analysis from five sites, we make an important observation that there are strong patterns with user mobility. Motivated by this observation, we propose a localization approach called EMP by exploiting mobility patterns of users for localization in sparse mobile networks. EMP implements a range-free distributed algorithm, with which each user collaboratively estimates its current location by fusing two localization sources, that is, network connectivity with other nodes and mobility patterns. With trace driven simulations, we demonstrate that EMP significantly improves the localization accuracy, comparing with other existing localization approaches.

A Two-hop Collaborative Localization Algorithm for Wireless Sensor Networks

Localization technology is one of the key supporting technologies in wireless sensor networks. In this paper, a two-hop collaborative multilateral localization algorithm is proposed to localization issues for wireless sensor networks. The algorithm applies anchor nodes within two hops to localize unknown nodes, and uses the minimum range error estimation method to compute coordinates of the unknown nodes. If an unknown node can not be localized through two-hop anchor nodes, it is localized by anchor nodes and localized nodes within two hops through auxiliary iterative localization method. Simulation results show that the localization accuracy of this algorithm is very good, even in larger range errors. DOI: http://dx.doi.org/10.11591/telkomnika.v11i5.2471

A Collaborative Multilateral Localization Algorithm for Wireless Sensor Networks

Localization technology is one of the key supporting technologies in wireless sensor networks. In this paper, a collaborative multilateral localization algorithm is proposed to localization issues for wireless sensor networks. The algorithm applies anchor nodes within two hops to localize unknown nodes, and uses Nelder-Mead simplex optimization method to compute coordinates of the unknown nodes. If an unknown node can not be localized through two-hop anchor nodes, it is localized by anchor nodes and localized nodes within two hops through auxiliary iterative localization method. Simulation results show that the localization accuracy of this algorithm is very good, even in larger range errors.

ECOPS: Energy-Efficient Collaborative Opportunistic Positioning for Heterogeneous Mobile Devices

The fast growing popularity of smartphones and tablets enables us to use various intelligent mobile applications. As many of those applications require position information, smart mobile devices provide positioning methods such as Global Positioning System (GPS), WiFi-based positioning system (WPS), or Cell-ID-based positioning service. However, those positioning methods have different characteristics of energy-efficiency, accuracy, and service availability. In this paper, we present an Energy-Efficient Collaborative and Opportunistic Positioning System (ECOPS) for heterogeneous mobile devices. ECOPS facilitates a collaborative environment where many mobile devices can opportunistically receive position information over energy-efficient and prevalent WiFi, broadcasted from a few other devices in the communication range. The position-broadcasting devices in ECOPS have sufficient battery power and up-to-date location information obtained from accurate but energy-inefficient GPS. A position receiver in ECOPS estimates its location using a combination of methods including received signal strength indicators and 2D trilateration. Our field experiments show that ECOPS significantly reduces the total energy consumption of devices while achieving an acceptable level of location accuracy. ECOPS can be especially useful for unique resource scarce, infrastructureless, and mission critical scenarios such as battlefields, border patrol, mountaineering expeditions, and disaster area assistance.

Multirobot Localization in Highly Symmetrical Environments

The paper addresses and solves the problem of multirobot collaborative localization in highly symmetrical 2D environments, such as the ones encountered in logistic applications. Because of the environment symmetry, the most common localization algorithms may fail to provide a correct estimate of the position and orientation of the robot, if its initial position is not known, no specific landmark is introduced, and no absolute information (e.g., GPS) is available: the robot can estimate its position with respect to the walls of the corridor, but it could be critical to determine in which corridor it is actually moving. The proposed algorithm is based upon a particle filter cooperative Monte Carlo Localization (MCL) and implements a three-stage procedure for the global localization and the accurate position tracking of each robot of the team. Online simulations and experimental tests, which investigate different situations with respect to the number of robots involved and their initial positions, show how the proposed solution can lead to the global localization of each robot, with a precision sufficient to be used as starting point for the subsequent robot tracking.

Collaborative positioning and embedded multi-sensors fusion cooperation in advanced driver assistance system

In this paper, we propose two different applications in the area of V2V communications. First, we present a method for better car tracking using GPS information shared through the V2V communication and a vision system in order to support accurate positioning. To accomplish this, we propose to useparticle filtering techniques, and when GPS data is unavailable, or of poor quality, we couple GPS data with vision data collected from the vehicles. Second, we present a new simulated framework for prototyping the whole process by combiningembedded data, vision data and V2V simulations to progress toward an anti-collision application. This framework could provide a better understanding of road security by studying the impact of V2V communications, thereby improving the quality of perception systems and adding new features for “ADAS”. Our experimentations have been conducted in different scenarios on a fleet of vehicles moving and communicating in real-time conditions. The obtained results demonstrate the consistency of our method whenever the GPS is unavailable. Moreover, they prove the feasibility and the performance efficiency of such real-time multisensory fusion to provide an integrated framework for collision avoidance.