Mobility increases localizability: A survey on wireless indoor localization using inertial sensors

Wireless indoor positioning has been extensively studied for the past 2 decades and continuously attracted growing research efforts in mobile computing context. As the integration of multiple inertial sensors (e.g., accelerometer, gyroscope, and magnetometer) to nowadays smartphones in recent years, human-centric mobility sensing is emerging and coming into vogue. Mobility information, as a new dimension in addition to wireless signals, can benefit localization in a number of ways, since location and mobility are by nature related in the physical world. In this article, we survey this new trend of mobility enhancing smartphone-based indoor localization. Specifically, we first study how to measure human mobility: what types of sensors we can use and what types of mobility information we can acquire. Next, we discuss how mobility assists localization with respect to enhancing location accuracy, decreasing deployment cost, and enriching location context. Moreover, considering the quality and cost of smartphone built-in sensors, handling measurement errors is essential and accordingly investigated. Combining existing work and our own working experiences, we emphasize the principles and conduct comparative study of the mainstream technologies. Finally, we conclude this survey by addressing future research directions and opportunities in this new and largely open area.</jats:p

On the Use of Graphs for Node Connectivity in Wireless Sensor Networks for Hostile Environments

[EN] Wireless sensor networks (WSNs) have been extensively studied in the literature. However, in hostile environments where node connectivity is severely compromised, the system performance can be greatly affected. In this work, we consider such a hostile environment where sensor nodes cannot directly communicate to some neighboring nodes. Building on this, we propose a distributed data gathering scheme where data packets are stored in different nodes throughout the network instead to considering a single sink node. As such, if nodes are destroyed or damaged, some information can still be retrieved. To evaluate the performance of the system, we consider the properties of different graphs that describe the connections among nodes. It is shown that the degree distribution of the graph has an important impact on the performance of the system. A teletraffic analysis is developed to study the average buffer size and average packet delay. To this end, we propose a reference node approach, which entails an approximation for the mathematical modeling of these networks that effectively simplifies the analysis and approximates the overall performance of the system.The authors wish to thank the Consejo Nacional de Ciencia y Tecnologia (CONACyT), the Comision de Operacion y Fomento de Actividades Academicas, Instituto Politecnico Nacional (COFAA-IPN, project numbers 20196225 and 20196678), and the Estimulos al Desempeno de los Investigadores del Instituto Politecnico Nacional (EDI-IPN) for the support given for this work. The work of V. Pla was supported by Grant PGC2018-094151-B-I00 (MCIU/AEI/FEDER, UE).García-González, E.; Chimal-Eguía, JC.; Rivero-Angeles, ME.; Pla, V. (2019). On the Use of Graphs for Node Connectivity in Wireless Sensor Networks for Hostile Environments. Journal of Sensors. 2019:1-22. https://doi.org/10.1155/2019/7409329S1222019Eren, T. (2017). The effects of random geometric graph structure and clustering on localizability of sensor networks. International Journal of Distributed Sensor Networks, 13(12), 155014771774889. doi:10.1177/1550147717748898Clauset, A., Shalizi, C. R., & Newman, M. E. J. (2009). Power-Law Distributions in Empirical Data. SIAM Review, 51(4), 661-703. doi:10.1137/070710111Hakimi, S. L. (1962). On Realizability of a Set of Integers as Degrees of the Vertices of a Linear Graph. I. Journal of the Society for Industrial and Applied Mathematics, 10(3), 496-506. doi:10.1137/011003

Euclidean distance geometry and applications

Euclidean distance geometry is the study of Euclidean geometry based on the concept of distance. This is useful in several applications where the input data consists of an incomplete set of distances, and the output is a set of points in Euclidean space that realizes the given distances. We survey some of the theory of Euclidean distance geometry and some of the most important applications: molecular conformation, localization of sensor networks and statics.Comment: 64 pages, 21 figure

Visual Place Recognition in Changing Environments

Localization is an essential capability of mobile robots and place recognition is an important component of localization. Only having precise localization, robots can reliably plan, navigate and understand the environment around them. The main task of visual place recognition algorithms is to recognize based on the visual input if the robot has seen previously a given place in the environment. Cameras are one of the popular sensors robots get information from. They are lightweight, affordable, and provide detailed descriptions of the environment in the form of images. Cameras are shown to be useful for the vast variety of emerging applications, from virtual and augmented reality applications to autonomous cars or even fleets of autonomous cars. All these applications need precise localization. Nowadays, the state-of-the-art methods are able to reliably estimate the position of the robots using image streams. One of the big challenges still is the ability to localize a camera given an image stream in the presence of drastic visual appearance changes in the environment. Visual appearance changes may be caused by a variety of different reasons, starting from camera-related factors, such as changes in exposure time, camera position-related factors, e.g. the scene is observed from a different position or viewing angle, occlusions, as well as factors that stem from natural sources, for example seasonal changes, different weather conditions, illumination changes, etc. These effects change the way the same place in the environments appears in the image and can lead to situations where it becomes hard even for humans to recognize the places. Also, the performance of the traditional visual localization approaches, such as FABMAP or DBow, decreases dramatically in the presence of strong visual appearance changes. The techniques presented in this thesis aim at improving visual place recognition capabilities for robotic systems in the presence of dramatic visual appearance changes. To reduce the effect of visual changes on image matching performance, we exploit sequences of images rather than individual images. This becomes possible as robotic systems collect data sequentially and not in random order. We formulate the visual place recognition problem under strong appearance changes as a problem of matching image sequences collected by a robotic system at different points in time. A key insight here is the fact that matching sequences reduces the ambiguities in the data associations. This allows us to establish image correspondences between different sequences and thus recognize if two images represent the same place in the environment. To perform a search for image correspondences, we construct a graph that encodes the potential matches between the sequences and at the same time preserves the sequentiality of the data. The shortest path through such a data association graph provides the valid image correspondences between the sequences. Robots operating reliably in an environment should be able to recognize a place in an online manner and not after having recorded all data beforehand. As opposed to collecting image sequences and then determining the associations between the sequences offline, a real-world system should be able to make a decision for every incoming image. In this thesis, we therefore propose an algorithm that is able to perform visual place recognition in changing environments in an online fashion between the query and the previously recorded reference sequences. Then, for every incoming query image, our algorithm checks if the robot is in the previously seen environment, i.e. there exists a matching image in the reference sequence, as well as if the current measurement is consistent with previously obtained query images. Additionally, to be able to recognize places in an online manner, a robot needs to recognize the fact that it has left the previously mapped area as well as relocalize when it re-enters environment covered by the reference sequence. Thus, we relax the assumption that the robot should always travel within the previously mapped area and propose an improved graph-based matching procedure that allows for visual place recognition in case of partially overlapping image sequences. To achieve a long-term autonomy, we further increase the robustness of our place recognition algorithm by incorporating information from multiple image sequences, collected along different overlapping and non-overlapping routes. This allows us to grow the coverage of the environment in terms of area as well as various scene appearances. The reference dataset then contains more images to match against and this increases the probability of finding a matching image, which can lead to improved localization. To be able to deploy a robot that performs localization in large scaled environments over extended periods of time, however, collecting a reference dataset may be a tedious, resource consuming and in some cases intractable task. Avoiding an explicit map collection stage fosters faster deployment of robotic systems in the real world since no map has to be collected beforehand. By using our visual place recognition approach the map collection stage can be skipped, as we are able to incorporate the information from a publicly available source, e.g., from Google Street View, into our framework due to its general formulation. This automatically enables us to perform place recognition on already existing publicly available data and thus avoid costly mapping phase. In this thesis, we additionally show how to organize the images from the publicly available source into the sequences to perform out-of-the-box visual place recognition without previously collecting the otherwise required reference image sequences at city scale. All approaches described in this thesis have been published in peer-reviewed conference papers and journal articles. In addition to that, most of the presented contributions have been released publicly as open source software

Multi-SLAM Systems for Fault-Tolerant Simultaneous Localization and Mapping

Mobile robots need accurate, high fidelity models of their operating environments in order to complete their tasks safely and efficiently. Generating these models is most often done via Simultaneous Localization and Mapping (SLAM), a paradigm where the robot alternatively estimates the most up-to-date model of the environment and its position relative to this model as it acquires new information from its sensors over time. Because robots operate in many different environments with different compute, memory, sensing, and form constraints, the nature and quality of information available to individual instances of different SLAM systems varies substantially. `One-size-fits-all\u27 solutions are thus exceedingly difficult to engineer, and highly specialized systems, which represent the state-of-the-art for most types of deployments, are not robust to operating conditions in which their assumptions are not met. This thesis seeks to investigate an alternative approach to these robustness and universality problems by incorporating existing SLAM solutions within a larger framework supported by planning and learning. The central idea is to combine learned models that estimate SLAM algorithm performance under a variety of sensory conditions, in this case neural networks, with planners designed for planning under uncertainty and partial observability, in this case partially observable Markov decision problems (POMDPs). Models of existing SLAM algorithms can be learned, and these models can then be used online to estimate the performance of a range of solutions to the SLAM problem at hand. The POMDP policy then selects the appropriate algorithm, given the estimated performance, cost of switching methods, and other information. This general approach may also be applicable to many other robotics problems that rely on data-fusion, such as grasp planning, motion planning, or object identification

Localization in Wireless Sensor Networks

International audienceWith the proliferation of Wireless Sensor Networks (WSN) applications, knowing the node current location have become a crucial requirement. Location awareness enables various applications from object tracking to event monitoring, and also supports core network services such as: routing, topology control, coverage, boundary detection and clustering. Therefore, WSN localization have become an important area that attracted significant research interest. In the most common case, position related parameters are first extracted from the received measurements, and then used in a second step for estimating the position of the tracked node by means of a specific algorithm. From this perspective, this chapter is intended to provide an overview of the major localization techniques, in order to provide the reader with the necessary inputs to quickly understand the state-of-the-art and/or apply these techniques to localization problems such as robot networks. We first review the most common measurement techniques, and study their theoretical accuracy limits in terms of Cramer-Rao lower bounds. Secondly, we classify the main localization algorithms, taking those measurements as input in order to provide an estimated position of the tracked node(s)

Indoor navigation systems based on data mining techniques in internet of things: a survey

© 2018, Springer Science+Business Media, LLC, part of Springer Nature. Internet of Things (IoT) is turning into an essential part of daily life, and numerous IoT-based scenarios will be seen in future of modern cities ranging from small indoor situations to huge outdoor environments. In this era, navigation continues to be a crucial element in both outdoor and indoor environments, and many solutions have been provided in both cases. On the other side, recent smart objects have produced a substantial amount of various data which demands sophisticated data mining solutions to cope with them. This paper presents a detailed review of previous studies on using data mining techniques in indoor navigation systems for the loT scenarios. We aim to understand what type of navigation problems exist in different IoT scenarios with a focus on indoor environments and later on we investigate how data mining solutions can provide solutions on those challenges

A Decentralized Architecture for Active Sensor Networks

This thesis is concerned with the Distributed Information Gathering (DIG) problem in which a Sensor Network is tasked with building a common representation of environment. The problem is motivated by the advantages offered by distributed autonomous sensing systems and the challenges they present. The focus of this study is on Macro Sensor Networks, characterized by platform mobility, heterogeneous teams, and long mission duration. The system under consideration may consist of an arbitrary number of mobile autonomous robots, stationary sensor platforms, and human operators, all linked in a network. This work describes a comprehensive framework called Active Sensor Network (ASN) which addresses the tasks of information fusion, decistion making, system configuration, and user interaction. The main design objectives are scalability with the number of robotic platforms, maximum flexibility in implementation and deployment, and robustness to component and communication failure. The framework is described from three complementary points of view: architecture, algorithms, and implementation. The main contribution of this thesis is the development of the ASN architecture. Its design follows three guiding principles: decentralization, modularity, and locality of interactions. These principles are applied to all aspects of the architecture and the framework in general. To achieve flexibility, the design approach emphasizes interactions between components rather than the definition of the components themselves. The architecture specifies a small set of interfaces sufficient to implement a wide range of information gathering systems. In the area of algorithms, this thesis builds on the earlier work on Decentralized Data Fusion (DDF) and its extension to information-theoretic decistion making. It presents the Bayesian Decentralized Data Fusion (BDDF) algorithm formulated for environment features represented by a general probability density function. Several specific representations are also considered: Gaussian, discrete, and the Certainty Grid map. Well known algorithms for these representations are shown to implement various aspects of the Bayesian framework. As part of the ASN implementation, a practical indoor sensor network has been developed and tested. Two series of experiments were conducted, utilizing two types of environment representation: 1) point features with Gaussian position uncertainty and 2) Certainty Grid maps. The network was operational for several days at a time, with individual platforms coming on and off-line. On several occasions, the network consisted of 39 software components. The lessons learned during the system's development may be applicable to other heterogeneous distributed systems with data-intensive algorithms

Improvement Schemes for Indoor Mobile Location Estimation: A Survey

Location estimation is significant in mobile and ubiquitous computing systems. The complexity and smaller scale of the indoor environment impose a great impact on location estimation. The key of location estimation lies in the representation and fusion of uncertain information from multiple sources. The improvement of location estimation is a complicated and comprehensive issue. A lot of research has been done to address this issue. However, existing research typically focuses on certain aspects of the problem and specific methods. This paper reviews mainstream schemes on improving indoor location estimation from multiple levels and perspectives by combining existing works and our own working experiences. Initially, we analyze the error sources of common indoor localization techniques and provide a multilayered conceptual framework of improvement schemes for location estimation. This is followed by a discussion of probabilistic methods for location estimation, including Bayes filters, Kalman filters, extended Kalman filters, sigma-point Kalman filters, particle filters, and hidden Markov models. Then, we investigate the hybrid localization methods, including multimodal fingerprinting, triangulation fusing multiple measurements, combination of wireless positioning with pedestrian dead reckoning (PDR), and cooperative localization. Next, we focus on the location determination approaches that fuse spatial contexts, namely, map matching, landmark fusion, and spatial model-aided methods. Finally, we present the directions for future research