Past, Present, and Future of Simultaneous Localization And Mapping: Towards the Robust-Perception Age

Simultaneous Localization and Mapping (SLAM)consists in the concurrent construction of a model of the environment (the map), and the estimation of the state of the robot moving within it. The SLAM community has made astonishing progress over the last 30 years, enabling large-scale real-world applications, and witnessing a steady transition of this technology to industry. We survey the current state of SLAM. We start by presenting what is now the de-facto standard formulation for SLAM. We then review related work, covering a broad set of topics including robustness and scalability in long-term mapping, metric and semantic representations for mapping, theoretical performance guarantees, active SLAM and exploration, and other new frontiers. This paper simultaneously serves as a position paper and tutorial to those who are users of SLAM. By looking at the published research with a critical eye, we delineate open challenges and new research issues, that still deserve careful scientific investigation. The paper also contains the authors' take on two questions that often animate discussions during robotics conferences: Do robots need SLAM? and Is SLAM solved

Internet of Things Spectrum Monitoring and Localization

This report presents the implementation of a system which localizes indoor sub-1GHz transmission signals and provides a low cost and effective alternative to traditional signal sensing and localization. The system uses a pre-existing WiFi network, PlutoSDRs, and a web server to visualize the estimated location of the transmitted signals. The system achieved 2.5-meter accuracy under ideal conditions satisfying the goal of 3-meter accuracy set forth by the team

Using iBeacon for Navigation and Proximity Awareness in Smart Buildings

Guests in unfamiliar buildings often do not know where to go to or what is around them. This project sought to alleviate this problem using a combination of smartphone and iBeacon technology. Received signal strength (RSS) information was obtained from iBeacon devices deployed in Atwater Kent Labs on the WPI campus. This information was fed into an Android application, which localized the user using a least mean squares (LMS) algorithm and provided contextual information to the user about their surroundings

On Random Sampling for Compliance Monitoring in Opportunistic Spectrum Access Networks

In the expanding spectrum marketplace, there has been a long term evolution towards more market€“oriented mechanisms, such as Opportunistic Spectrum Access (OSA), enabled through Cognitive Radio (CR) technology. However, the potential of CR technologies to revolutionize wireless communications, also introduces challenges based upon the potentially non€“deterministic CR behaviour in the Electrospace. While establishing and enforcing compliance to spectrum etiquette rules are essential to realization of successful OSA networks in the future, there has only been recent increased research activity into enforcement. This dissertation presents novel work on the spectrum monitoring aspect, which is crucial to effective enforcement of OSA. An overview of the challenges faced by current compliance monitoring methods is first presented. A framework is then proposed for the use of random spectral sampling techniques to reduce data collection complexity in wideband sensing scenarios. This approach is recommended as an alternative to Compressed Sensing (CS) techniques for wideband spectral occupancy estimation, which may be difficult to utilize in many practical congested scenarios where compliance monitoring is required. Next, a low€“cost computational approach to online randomized temporal sensing deployment is presented for characterization of temporal spectrum occupancy in cognitive radio scenarios. The random sensing approach is demonstrated and its performance is compared to CS€“based approach for occupancy estimation. A novel frame€“based sampling inversion technique is then presented for cases when it is necessary to track the temporal behaviour of individual CRs or CR networks. Parameters from randomly sampled Physical Layer Convergence Protocol (PLCP) data frames are used to reconstruct occupancy statistics, taking account of missed frames due to sampling design, sensor limitations and frame errors. Finally, investigations into the use of distributed and mobile spectrum sensing to collect spatial diversity to improve the above techniques are presented, for several common monitoring tasks in spectrum enforcement. Specifically, focus is upon techniques for achieving consensus in dynamic topologies such as in mobile sensing scenarios

Autonomous and Resilient Management of All-Source Sensors for Navigation Assurance

All-source navigation has become increasingly relevant over the past decade with the development of viable alternative sensor technologies. However, as the number and type of sensors informing a system increases, so does the probability of corrupting the system with sensor modeling errors, signal interference, and undetected faults. Though the latter of these has been extensively researched, the majority of existing approaches have constrained faults to biases, and designed algorithms centered around the assumption of simultaneously redundant, synchronous sensors with valid measurement models, none of which are guaranteed for all-source systems. This research aims to provide all-source multi-sensor resiliency, assurance, and integrity through an autonomous sensor management framework. The proposed framework dynamically places each sensor in an all-source system into one of four modes: monitoring, validation, calibration, and remodeling. Each mode contains specific and novel realtime processes that affect how a navigation system responds to sensor measurements. The monitoring mode is driven by a novel sensor-agnostic fault detection, exclusion, and integrity monitoring method that minimizes the assumptions on the fault type, all-source sensor composition, and the number of faulty sensors. The validation mode provides a novel method for the online validation of sensors which have questionable sensor models, in a fault-agnostic and sensor-agnostic manner, and without compromising the ongoing navigation solution in the process. The remaining two modes, calibration and remodeling, generalize and integrate online calibration and model identification processes to provide autonomous and dynamic estimation of candidate model functions and their parameters, which when paired with the monitoring and validation processes, directly enable resilient, self-correcting, plug-and-play open architecture navigation systems

D6.2 - Prototype description and field trial results

Deliverable D6.2 del projecte FARAMIRPostprint (published version

Bidirectional UWB Localization: A Review on an Elastic Positioning Scheme for GNSS-deprived Zones

A bidirectional Ultra-Wideband (UWB) localization scheme is one of the three widely deployed design integration processes ordinarily destined for time-based UWB positioning systems. The key property of the bidirectional UWB localization is its ability to serve both the navigation and tracking assignments on-demand within a single localization scheme. Conventionally, the perspective of navigation and tracking in wireless localization systems is viewed distinctly as an individual system because different methodologies were required for the implementation process. The ability to flexibly or elastically combine two unique positioning perspectives (i.e., navigation and tracking) within a single scheme is a paradigm shift in the way location-based services are observed. Thus, this article addresses and pinpoints the potential of a bidirectional UWB localization scheme. Regarding this, the complete system model of the bidirectional UWB localization scheme was comprehensively described based on modular processes in this article. The demonstrative evaluation results based on two system integration processes as well as a SWOT (strengths, weaknesses, opportunities, and threats) analysis of the scheme were also discussed. Moreover, we argued that the presented bidirectional scheme can also be used as a prospective topology for the realization of precise location estimation processes in 5G/6G wireless mobile networks, as well as Wi-Fi fine-time measurement-based positioning systems in this article.Comment: 30 pages, 12 figure

Characterization and Design Methodologies for Wearable Passive UHF RFID Tag Antennas for Wireless Body-Centric Systems

Radio Frequency Identification (RFID) is a wireless automatic identification technology that utilizes electrically active tags – low-cost and low-power wireless communication devices that let themselves transparently and unobstructively be embedded into everyday objects to remotely track information of the object’s physical location, origin, and ownership. At ultra-high frequencies (UHF), this technology uses propagating electromagnetic waves for communication, which enables the fast identification of tags at large distances. A passive RFID tag includes two main components; a tag antenna and an RFID integrate circuit (tag IC). A passive tag relies solely on the external power harvested from an incident electromagnetic wave to run its circuitry and for data transmission. The passiveness makes the tag maintenance-free, simple, and low-cost, allowing large-scale commercial applications in the supply chain, ticketing, and asset tracking. The future of RFID, however, lies in the transition from traditional embedded applications to wearable intelligent systems, in which the tags are seamlessly integrated with everyday clothing. Augmented with various ambient and biochemical sensors, the tag is capable of detecting physical parameters of its environment and providing continuous monitoring of human vital signs. Tremendous amount of tagged entities establish an intelligent infrastructure that is personalized and tailored to the needs of each individual and ultimately, it recedes into the background of our daily life. Although wearable tags in intelligent systems have the enormous potential to revolutionize the quality of human life, the emerging wearable RFID applications introduce new challenges for designers developing efficient and sophisticated RFID systems. Traditional tag design parameters and solutions will no longer respond to the new requirements. Instead, the whole RF community must adopt new methods and unconventional approaches to achieve advanced wearable tags that are highly transparently integrated into our daily life. In this research work, an empirical as well as a theoretical approach is taken to address the above-mentioned wearable RFID tag challenges. Exploiting new analysis tools in combination with computational electromagnetics, a novel technique to model the human body in UHF applications for initiating the design of optimized wearable tags is developed. Further, fundamental unprecedented UHF characteristics of advanced wearable electronics materials – electro-textiles, are established. As an extremely important outcome of this research work, innovative optimization methodologies for the promotion of novel and advanced wearable UHF antennas are proposed. Particularly, it is evidenced that proper embroidery fabrication techniques have the great potential to realize wearable tag antennas exhibiting excellent RF performance and structural properties for the seamless integration with clothing. The kernel of this research work is the realization of a flexible and fully embroidered passive UHF RFID patch tag prototype achieving optimized performance in close vicinity of the high-permittivity and dissipative human body. Its performance may be considered as a benchmark for future wearable antenna designs. This shows that this research work outcome forms an important contribution to the state of the art and a milestone in the development towards wearable intelligence