RF Localization in Indoor Environment

In this paper indoor localization system based on the RF power measurements of the Received Signal Strength (RSS) in WLAN environment is presented. Today, the most viable solution for localization is the RSS fingerprinting based approach, where in order to establish a relationship between RSS values and location, different machine learning approaches are used. The advantage of this approach based on WLAN technology is that it does not need new infrastructure (it reuses already and widely deployed equipment), and the RSS measurement is part of the normal operating mode of wireless equipment. We derive the Cramer-Rao Lower Bound (CRLB) of localization accuracy for RSS measurements. In analysis of the bound we give insight in localization performance and deployment issues of a localization system, which could help designing an efficient localization system. To compare different machine learning approaches we developed a localization system based on an artificial neural network, k-nearest neighbors, probabilistic method based on the Gaussian kernel and the histogram method. We tested the developed system in real world WLAN indoor environment, where realistic RSS measurements were collected. Experimental comparison of the results has been investigated and average location estimation error of around 2 meters was obtained

Milli-RIO: Ego-Motion Estimation with Low-Cost Millimetre-Wave Radar

Robust indoor ego-motion estimation has attracted significant interest in the last decades due to the fast-growing demand for location-based services in indoor environments. Among various solutions, frequency-modulated continuous-wave (FMCW) radar sensors in millimeter-wave (MMWave) spectrum are gaining more prominence due to their intrinsic advantages such as penetration capability and high accuracy. Single-chip low-cost MMWave radar as an emerging technology provides an alternative and complementary solution for robust ego-motion estimation, making it feasible in resource-constrained platforms thanks to low-power consumption and easy system integration. In this paper, we introduce Milli-RIO, an MMWave radar-based solution making use of a single-chip low-cost radar and inertial measurement unit sensor to estimate six-degrees-of-freedom ego-motion of a moving radar. Detailed quantitative and qualitative evaluations prove that the proposed method achieves precisions on the order of few centimeters for indoor localization tasks.Comment: Submitted to IEEE Sensors, 9page

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

Application of Channel Modeling for Indoor Localization Using TOA and RSS

Recently considerable attention has been paid to indoor geolocation using wireless local area networks (WLAN) and wireless personal area networks (WPAN) devices. As more applications using these technologies are emerging in the market, the need for accurate and reliable localization increases. In response to this need, a number of technologies and associated algorithms have been introduced in the literature. These algorithms resolve the location either by using estimated distances between a mobile station (MS) and at least three reference points (via triangulation) or pattern recognition through radio frequency (RF) fingerprinting. Since RF fingerprinting, which requires on site measurements is a time consuming process, it is ideal to replace this procedure with the results obtained from radio channel modeling techniques. Localization algorithms either use the received signal strength (RSS) or time of arrival (TOA) of the received signal as their localization metric. TOA based systems are sensitive to the available bandwidth, and also to the occurrence of undetected direct path (UDP) channel conditions, while RSS based systems are less sensitive to the bandwidth and more resilient to UDP conditions. Therefore, the comparative performance evaluation of different positioning systems is a multifaceted and challenging problem. This dissertation demonstrates the viability of radio channel modeling techniques to eliminate the costly fingerprinting process in pattern recognition algorithms by introducing novel ray tracing (RT) assisted RSS and TOA based algorithms. Two sets of empirical data obtained by radio channel measurements are used to create a baseline for comparative performance evaluation of localization algorithms. The first database is obtained by WiFi RSS measurements in the first floor of the Atwater Kent laboratory; an academic building on the campus of WPI; and the other by ultra wideband (UWB) channel measurements in the third floor of the same building. Using the results of measurement campaign, we specifically analyze the comparative behavior of TOA- and RSS-based indoor localization algorithms employing triangulation or pattern recognition with different bandwidths adopted in WLAN and WPAN systems. Finally, we introduce a new RT assisted hybrid RSS-TOA based algorithm which employs neural networks. The resulting algorithm demonstrates a superior performance compared to the conventional RSS and TOA based algorithms in wideband systems

Integrasjon av et minimalistisk sett av sensorer for kartlegging og lokalisering av landbruksroboter

Robots have recently become ubiquitous in many aspects of daily life. For in-house applications there is vacuuming, mopping and lawn-mowing robots. Swarms of robots have been used in Amazon warehouses for several years. Autonomous driving cars, despite being set back by several safety issues, are undeniably becoming the standard of the automobile industry. Not just being useful for commercial applications, robots can perform various tasks, such as inspecting hazardous sites, taking part in search-and-rescue missions. Regardless of end-user applications, autonomy plays a crucial role in modern robots. The essential capabilities required for autonomous operations are mapping, localization and navigation. The goal of this thesis is to develop a new approach to solve the problems of mapping, localization, and navigation for autonomous robots in agriculture. This type of environment poses some unique challenges such as repetitive patterns, large-scale sparse features environments, in comparison to other scenarios such as urban/cities, where the abundance of good features such as pavements, buildings, road lanes, traffic signs, etc., exists. In outdoor agricultural environments, a robot can rely on a Global Navigation Satellite System (GNSS) to determine its whereabouts. It is often limited to the robot's activities to accessible GNSS signal areas. It would fail for indoor environments. In this case, different types of exteroceptive sensors such as (RGB, Depth, Thermal) cameras, laser scanner, Light Detection and Ranging (LiDAR) and proprioceptive sensors such as Inertial Measurement Unit (IMU), wheel-encoders can be fused to better estimate the robot's states. Generic approaches of combining several different sensors often yield superior estimation results but they are not always optimal in terms of cost-effectiveness, high modularity, reusability, and interchangeability. For agricultural robots, it is equally important for being robust for long term operations as well as being cost-effective for mass production. We tackle this challenge by exploring and selectively using a handful of sensors such as RGB-D cameras, LiDAR and IMU for representative agricultural environments. The sensor fusion algorithms provide high precision and robustness for mapping and localization while at the same time assuring cost-effectiveness by employing only the necessary sensors for a task at hand. In this thesis, we extend the LiDAR mapping and localization methods for normal urban/city scenarios to cope with the agricultural environments where the presence of slopes, vegetation, trees render the traditional approaches to fail. Our mapping method substantially reduces the memory footprint for map storing, which is important for large-scale farms. We show how to handle the localization problem in dynamic growing strawberry polytunnels by using only a stereo visual-inertial (VI) and depth sensor to extract and track only invariant features. This eliminates the need for remapping to deal with dynamic scenes. Also, for a demonstration of the minimalistic requirement for autonomous agricultural robots, we show the ability to autonomously traverse between rows in a difficult environment of zigzag-liked polytunnel using only a laser scanner. Furthermore, we present an autonomous navigation capability by using only a camera without explicitly performing mapping or localization. Finally, our mapping and localization methods are generic and platform-agnostic, which can be applied to different types of agricultural robots. All contributions presented in this thesis have been tested and validated on real robots in real agricultural environments. All approaches have been published or submitted in peer-reviewed conference papers and journal articles.Roboter har nylig blitt standard i mange deler av hverdagen. I hjemmet har vi støvsuger-, vaske- og gressklippende roboter. Svermer med roboter har blitt brukt av Amazons varehus i mange år. Autonome selvkjørende biler, til tross for å ha vært satt tilbake av sikkerhetshensyn, er udiskutabelt på vei til å bli standarden innen bilbransjen. Roboter har mer nytte enn rent kommersielt bruk. Roboter kan utføre forskjellige oppgaver, som å inspisere farlige områder og delta i leteoppdrag. Uansett hva sluttbrukeren velger å gjøre, spiller autonomi en viktig rolle i moderne roboter. De essensielle egenskapene for autonome operasjoner i landbruket er kartlegging, lokalisering og navigering. Denne type miljø gir spesielle utfordringer som repetitive mønstre og storskala miljø med få landskapsdetaljer, sammenlignet med andre steder, som urbane-/bymiljø, hvor det finnes mange landskapsdetaljer som fortau, bygninger, trafikkfelt, trafikkskilt, etc. I utendørs jordbruksmiljø kan en robot bruke Global Navigation Satellite System (GNSS) til å navigere sine omgivelser. Dette begrenser robotens aktiviteter til områder med tilgjengelig GNSS signaler. Dette vil ikke fungere i miljøer innendørs. I ett slikt tilfelle vil reseptorer mot det eksterne miljø som (RGB-, dybde-, temperatur-) kameraer, laserskannere, «Light detection and Ranging» (LiDAR) og propriopsjonære detektorer som treghetssensorer (IMU) og hjulenkodere kunne brukes sammen for å bedre kunne estimere robotens tilstand. Generisk kombinering av forskjellige sensorer fører til overlegne estimeringsresultater, men er ofte suboptimale med hensyn på kostnadseffektivitet, moduleringingsgrad og utbyttbarhet. For landbruksroboter så er det like viktig med robusthet for lang tids bruk som kostnadseffektivitet for masseproduksjon. Vi taklet denne utfordringen med å utforske og selektivt velge en håndfull sensorer som RGB-D kameraer, LiDAR og IMU for representative landbruksmiljø. Algoritmen som kombinerer sensorsignalene gir en høy presisjonsgrad og robusthet for kartlegging og lokalisering, og gir samtidig kostnadseffektivitet med å bare bruke de nødvendige sensorene for oppgaven som skal utføres. I denne avhandlingen utvider vi en LiDAR kartlegging og lokaliseringsmetode normalt brukt i urbane/bymiljø til å takle landbruksmiljø, hvor hellinger, vegetasjon og trær gjør at tradisjonelle metoder mislykkes. Vår metode reduserer signifikant lagringsbehovet for kartlagring, noe som er viktig for storskala gårder. Vi viser hvordan lokaliseringsproblemet i dynamisk voksende jordbær-polytuneller kan løses ved å bruke en stereo visuel inertiel (VI) og en dybdesensor for å ekstrahere statiske objekter. Dette eliminerer behovet å kartlegge på nytt for å klare dynamiske scener. I tillegg demonstrerer vi de minimalistiske kravene for autonome jordbruksroboter. Vi viser robotens evne til å bevege seg autonomt mellom rader i ett vanskelig miljø med polytuneller i sikksakk-mønstre ved bruk av kun en laserskanner. Videre presenterer vi en autonom navigeringsevne ved bruk av kun ett kamera uten å eksplisitt kartlegge eller lokalisere. Til slutt viser vi at kartleggings- og lokaliseringsmetodene er generiske og platform-agnostiske, noe som kan brukes med flere typer jordbruksroboter. Alle bidrag presentert i denne avhandlingen har blitt testet og validert med ekte roboter i ekte landbruksmiljø. Alle forsøk har blitt publisert eller sendt til fagfellevurderte konferansepapirer og journalartikler

Bounds on RF cooperative localization for video capsule endoscopy

Wireless video capsule endoscopy has been in use for over a decade and it uses radio frequency (RF) signals to transmit approximately fifty five thousands clear pictures of inside the GI tract to the body-mounted sensor array. However, physician has no clue on the exact location of the capsule inside the GI tract to associate it with the pictures showing abnormalities such as bleeding or tumors. It is desirable to use the same RF signal for localization of the VCE as it passes through the human GI tract. In this thesis, we address the accuracy limits of RF localization techniques for VCE localization applications. We present an assessment of the accuracy of cooperative localization of VCE using radio frequency (RF) signals with particular emphasis on localization inside the small intestine. We derive the Cramer-Rao Lower Bound (CRLB) for cooperative location estimators using the received signal strength(RSS) or the time of arrival (TOA) of the RF signal. Our derivations are based on a three-dimension human body model, an existing model for RSS propagation from implant organs to body surface and a TOA ranging error model for the effects of non-homogenity of the human body on TOA of the RF signals. Using models for RSS and TOA errors, we first calculate the 3D CRLB bounds for cooperative localization of the VCE in three major digestive organs in the path of GI tract: the stomach, the small intestine and the large intestine. Then we analyze the performance of localization techniques on a typical path inside the small intestine. Our analysis includes the effects of number of external sensors, the external sensor array topology, number of VCE in cooperation and the random variations in transmit power from the capsule

Modeling the Behavior of Multipath Components Pertinent to Indoor Geolocation

Recently, a number of empirical models have been introduced in the literature for the behavior of direct path used in the design of algorithms for RF based indoor geolocation. Frequent absence of direct path has been a major burden on the performance of these algorithms directing researchers to discover algorithms using multipath diversity. However, there is no reliable model for the behavior of multipath components pertinent to precise indoor geolocation. In this dissertation, we first examine the absence of direct path by statistical analysis of empirical data. Then we show how the concept of path persistency can be exploited to obtain accurate ranging using multipath diversity. We analyze the effects of building architecture on the multipath structure by demonstrating the effects of wall length and wall density on the path persistency. Finally, we introduce a comprehensive model for the spatial behavior of multipath components. We use statistical analysis of empirical data obtained by a measurement calibrated ray-tracing tool to model the time-of- arrival, angle-of-arrival and path gains. The relationship between the transmitter-receiver separation and the number of paths are also incorporated in our model. In addition, principles of ray optics are applied to explain the spatial evolution of path gains, time-of-arrival and angle-of-arrival of individual multipath components as a mobile terminal moves inside a typical indoor environment. We also use statistical modeling for the persistency and birth/death rate of the paths

Computation and Time constraints in Localization and Mapping Problems

Research on simultaneous localization and mapping problems has been extensively carried out by robotics community in the last decade and several subproblems –like data association, map representation, dynamic environments or semantic mapping– have been more or less deeply investigated. One of the most important questions is the online execution of localization and mapping methods. Since observations are periodically captured by robot sensors, localization and mapping algorithms are constrained to complete the execution of an update before a new observation is available. In literature, several partial contributions have been presented, most of them focused on the reduction of computational complexity, but no comprehensive discussion of real-time feasibility had been previously proposed. The reasons that make real-time feasibility difficult are different in the case of localization and of mapping problems, but a general criterion can be found. In this thesis we claim that a locality principle is a general design criterion for real-time or incremental execution of localization and mapping algorithms. The probabilistic robotics paradigm provides a unified formulation for the different problems and a conceptual framework for the application of the proposed criterion. Locality may be applied to perform temporal or spatial decomposition of the global estimation. This thesis provides a general perspective of real-time feasibility and the identification of locality principle as a general design criterion for algorithms to meet time constraints. The particular contributions of this thesis correspond to the application of the locality principle to specific problems. The Real-Time Particle Filter is an advanced version of Particle Filter algorithm conceived to achieve a tradeoff between time constraints and filter accuracy depending on the number of samples. This goal is achieved by partitioning the overall samples required to obtain the required accuracy into sets, each of them corresponding to an observation, and by reconstructing the new set at the end of an estimation window. We proposed two main contributions: first, an analysis of the efficiency of the resampling solution of the Real-Time Particle Filter through the concept of effective sample size; second, a method to compute the mixture weights that balances the the effective sample size of partition sets and is less prone to numerical instability. The second specific contribution is an incremental version of a maximum likelihood map estimator. The adopted technique combines stochastic gradient descent and incremental tree parameterization and exploits an efficient optimization technique and organizes the graph into a spanning tree structure suitable for a decomposition. In this thesis, the incremental version of the original algorithm has been adapted using again the locality principle. Local decomposition is achieved selecting the portion of the network perturbed by the addition of a new constraint. Furthermore, the perturbation of gradient descent iteration is limited for the region already converged by adapting the learning rate. Finally, optimization is scheduled with an heuristic rule that controls the error increase in the constraint network. The constraint solver has been integrated with a map builder that extracts the constraint network from laser scans and represents the environment with a metric-topological hybrid map. While real-time feasibility is not granted, the proposed incremental tree network optimizer is suitable for online execution and the algorithm converges faster than the previous version of the same algorithm and in several condition performs better than other state-of-the-art methods. The final contribution is a parallel maximum likelihood algorithm for robot mapping. The proposed algorithm estimates the map iterating a linearization step and the solution of the linear system with Gauss-Seidel relaxation. The network is divided in connected clusters of local nodes and the reorder induced by this decomposition transforms the linearized information matrix in block-border diagonal form. Each diagonal block of the matrix can then be solved independently. The proposed parallel maximum likelihood algorithm can exploit the computation resources provided by commodity multi-core processor. Moreover, this solution can be applied to multi-robot mapping. The contributions presented in this dissertation outline a novel perspective on real-time feasibility of robot localization and mapping methods, thus bringing these algorithmic techniques closer to applications

WiFi Fingerprinting Localization for Intelligent Vehicles in Car Park

International audienceIn this paper, a novel method of WiFi fingerprinting for localizing intelligent vehicles in GPS-denied area, such as car parks, is proposed. Although the method itself is a popular approach for indoor localization application, adapting it to the speed of vehicles requires different treatment. By deploying an ensemble neural network for fingerprinting classification, the method shows a reasonable localization precision at car park speed. Furthermore, a Gaussian Mixture Model (GMM) Particle Filter is applied to increase localization frequency as well as accuracy. Experiments show promising results with average localization error of 0.6m

Near Optimal Indoor Localization With Coherent Array Reconciliation Tomography

Our increased reliance on localization devices such as GPS navigation has led to an increased demand for localization solutions in all environments, including indoors. Indoor localization has received considerable attention in the last several years for a number of application areas including first responder localization to targeted advertising and social networking. The difficult multipath encountered indoors degrades the performance of RF based localization solutions and so far no optimal solution has been published. This dissertation presents an algorithm called Coherent Array Reconciliation Tomography (CART), which is a Direct Positioning Algorithm (DPA) that incorporates signal fusion to perform a simultaneous leading edge and position estimate for a superior localization solution in a high multipath environment. The CART algorithm produces position estimates that are near optimal in the sense that they achieve nearly the best theoretical accuracy possible using an Impulse Radio (IR) Ultra-Wideband (UWB) waveform. Several existing algorithms are compared to CART including a traditional two step Leading Edge Detection (LED) algorithm, Singular value Array Reconciliation Tomography (SART), and Transactional Array Reconciliation Tomography (TART) by simulation and experimentation. As shown under heavy simulated multipath conditions, where traditional LED produces a limited solution and the SART and TART algorithms fail, the CART algorithm produces a near statistically optimal solution. Finally, the CART algorithm was also successfully demonstrated experimentally in a laboratory environment by application to the fire fighter homing device that has been a part of the ongoing research at Worcester Polytechnic Institute (WPI)