Techniques for improving localization applications running on low-cost IoT devices

Nowadays, localization features are widespread in low-cost and low-power IoT applications such as bike-sharing,off-road vehicle fleet management, and theft prevention of smart devices. For such use cases, since the item to be tracked is inexpensive, older or power-constrained (e.g. battery-powered vehicles), localization features are realized by the installation of low-cost and low-power devices. In this paper, we describe a set of low-computational power techniques, targeting low-cost IoT devices, to process GPS and INS data for accomplishing specific and accurate localization and tracking tasks. The methods here proposed address the calibration of low-cost INS comprised of accelerometer and gyroscope without the aid of external sensors, correction of GPS drift when the target position is static,and the minimization of localization error at device boot. The performances of the proposed methods are then evaluated on several datasets acquired on the field and representing real use-case scenarios

Automated Driftmeter Fused with Inertial Navigation

The motivation of this research is to address the use of bearing-only measurements taken by an optical sensor to aid an Inertial Navigation System (INS) whose accelerometers and gyroscopes are subject to drift and bias errors. The concept of Simultaneous Localization And Mapping (SLAM) is employed in a bootstrapping manner: the bearing measurements are used to geolocate ground features, following which the bearings taken over time of the said ground features are used to improve the navigation state provided by the INS. In this research the INS aiding action of tracking stationary, but unknown, ground features over time is evaluated. It does not, however, address the critical image registration issue associated with image processing. It is assumed that stationary ground features are able to be detected and tracked as pixel representations by a real-time image processing algorithm. Simulations are performed which indicate the potential of this research. It is shown that during wings level flight at constant speed and fixed altitude, an aircraft that geolocates and tracks ground objects can significantly reduce the error in two of its three dimensions of flight, relative to an Earth-fixed navigation frame. The aiding action of geolocating and tracking ground features, in-line with the direction of flight, with a downward facing camera did not provide improvement in the aircraft\u27s x-position estimate. However, the aircraft\u27s y-position estimate, as well as the altitude estimate, were signicantly improved

Information Aided Navigation: A Review

The performance of inertial navigation systems is largely dependent on the stable flow of external measurements and information to guarantee continuous filter updates and bind the inertial solution drift. Platforms in different operational environments may be prevented at some point from receiving external measurements, thus exposing their navigation solution to drift. Over the years, a wide variety of works have been proposed to overcome this shortcoming, by exploiting knowledge of the system current conditions and turning it into an applicable source of information to update the navigation filter. This paper aims to provide an extensive survey of information aided navigation, broadly classified into direct, indirect, and model aiding. Each approach is described by the notable works that implemented its concept, use cases, relevant state updates, and their corresponding measurement models. By matching the appropriate constraint to a given scenario, one will be able to improve the navigation solution accuracy, compensate for the lost information, and uncover certain internal states, that would otherwise remain unobservable.Comment: 8 figures, 3 table

Preliminary design of a redundant strapped down inertial navigation unit using two-degree-of-freedom tuned-gimbal gyroscopes

This redundant strapdown INS preliminary design study demonstrates the practicality of a skewed sensor system configuration by means of: (1) devising a practical system mechanization utilizing proven strapdown instruments, (2) thoroughly analyzing the skewed sensor redundancy management concept to determine optimum geometry, data processing requirements, and realistic reliability estimates, and (3) implementing the redundant computers into a low-cost, maintainable configuration

Pilot Assisted Inertial Navigation System Aiding Using Bearings-Only Measurements Taken Over Time

The objective of this work is to develop an alternative INS aiding source other than the GPS, while preserving the autonomy of the integrated navigation system. It is proposed to develop a modernized method of aerial navigation using driftmeter measurements from an E/O system for ground feature tracking, and an independent altitude sensor in conjunction with the INS. The pilot will track a ground feature with the E/O system, while the aircraft is on autopilot holding constant airspeed, altitude, and heading during an INS aiding session. The ground feature measurements from the E/O system and the INS output form measurements provided to a linear KF running on the navigation computer to accomplish the INS aiding action. Aiding the INS will be periodically repeated as operationally permissible under pilot discretion. Little to no modeling error will be present when implementing the linear Kalman filter, indicating the strength of the INS aiding action will be exclusively determined by the prevailing degree of observability

Development and Validation of an IMU/GPS/Galileo Integration Navigation System for UAV

Several and distinct Unmanned Aircraft Vehicle (UAV) applications are emerging, demanding steps to be taken in order to allow those platforms to operate in an un-segregated airspace. The key risk component, hindering the widespread integration of UAV in an un-segregated airspace, is the autonomous component: the need for a high level of autonomy in the UAV that guarantees a safe and secure integration in an un-segregated airspace. At this point, the UAV accurate state estimation plays a fundamental role for autonomous UAV, being one of the main responsibilities of the onboard autopilot. Given the 21st century global economic paradigm, academic projects based on inexpensive UAV platforms but on expensive commercial autopilots start to become a non-economic solution. Consequently, there is a pressing need to overcome this problem through, on one hand, the development of navigation systems using the high availability of low cost, low power consumption, and small size navigation sensors offered in the market, and, on the other hand, using Global Navigation Satellite Systems Software Receivers (GNSS SR). Since the performance that is required for several applications in order to allow UAV to fly in an un-segregated airspace is not yet defined, for most UAV academic applications, the navigation system accuracy required should be at least the same as the one provided by the available commercial autopilots. This research focuses on the investigation of the performance of an integrated navigation system composed by a low performance inertial measurement unit (IMU) and a GNSS SR. A strapdown mechanization algorithm, to transform raw inertial data into navigation solution, was developed, implemented and evaluated. To fuse the data provided by the strapdown algorithm with the one provided by the GNSS SR, an Extended Kalman Filter (EKF) was implemented in loose coupled closed-loop architecture, and then evaluated. Moreover, in order to improve the performance of the IMU raw data, the Allan variance and denoise techniques were considered for both studying the IMU error model and improving inertial sensors raw measurements. In order to carry out the study, a starting question was made and then, based on it, eight questions were derived. These eight secondary questions led to five hypotheses, which have been successfully tested along the thesis. This research provides a deliverable to the Project of Research and Technologies on Unmanned Air Vehicles (PITVANT) Group, consisting of a well-documented UAV Development and Validation of an IMU/GPS/Galileo Integration Navigation System for UAV II navigation algorithm, an implemented and evaluated navigation algorithm in the MatLab environment, and Allan variance and denoising algorithms to improve inertial raw data, enabling its full implementation in the existent Portuguese Air Force Academy (PAFA) UAV. The derivable provided by this thesis is the answer to the main research question, in such a way that it implements a step by step procedure on how the Strapdown IMU (SIMU)/GNSS SR should be developed and implemented in order to replace the commercial autopilot. The developed integrated SIMU/GNSS SR solution evaluated, in post-processing mode, through van-test scenario, using real data signals, at the Galileo Test and Development Environment (GATE) test area in Berchtesgaden, Germany, when confronted with the solution provided by the commercial autopilot, proved to be of better quality. Although no centimetre-level of accuracy was obtained for the position and velocity, the results confirm that the integration strategy outperforms the Piccolo system performance, being this the ultimate goal of this research work

Study of Future On-board GNSS/INS Hybridization Architectures

Un développement rapide et une densification du trafic aérien ont conduit à l'introduction de nouvelles opérations d'approches et d'atterrissage utilisant des trajectoires plus flexibles et des minimas plus exigeants. La plupart des opérations de navigation aérienne sont actuellement réalisées grâce au GNSS, augmenté par les systèmes GBAS, SBAS et ABAS qui permettent d'atteindre des opérations d'approches de précision (pour GBAS et SBAS). Cependant ces systèmes nécessitent la mise en place d'un réseau de station de référence relativement couteux et des diffusions constantes de messages aux utilisateurs de l'espace aérien. Afin de surmonter ces contraintes, le système ABAS intègre à bord des informations fournies par les systèmes de navigation inertielle (INS) ainsi améliorant les performances de navigation. Dans cette logique, les avions commerciaux actuels utilisent une solution de couplage des deux systèmes appelée hybridation GPS/baro-INS. Cette solution permet d'atteindre des niveaux de performance en termes de précision, intégrité, disponibilité et continuité supérieurs aux deux systèmes pris séparément. Malheureusement, les niveaux d'exigences requis par les opérations de précision ou les atterrissages automatiques ne peuvent pas encore être totalement couverts par les solutions d'hybridation actuelles. L'idée principale de cette thèse a été d'étendre le processus d'hybridation en incluant d'autres capteurs ou systèmes actuellement disponibles ou non à bord et d'évaluer les niveaux de performance atteints par cette solution de filtre d'hybridation global. L'objectif ciblé est de pouvoir fournir la plupart des paramètres de navigations pour les opérations les plus critiques avec le niveau de performance requis par les exigences OACI. Les opérations ciblées pendant l'étude étaient les approches de précision (en particulier les approches CAT III) et le roulage sur la piste. L'étude des systèmes vidéo a fait l'objet d'une attention particulière pendant la thèse. La navigation basée sur la vidéo est une solution autonome de navigation de plus en plus utilisée de nos jours axée sur des capteurs qui mesurent le mouvement du véhicule et observent l'environnement. Que cela soit pour compenser la perte ou la dégradation d'un des systèmes de navigation ou pour améliorer la solution existante, les intérêts de l'utilisation de la vidéo sont nombreux. ABSTRACT : The quick development of air traffic has led to the improvement of approach and landing operations by using flexible flight paths and by decreasing the minima required to perform these operations. Most of the aircraft operations are supported by the GNSS augmented with GBAS, SBAS and ABAS. SBAS or GBAS allow supporting navigation operations down to precision approaches. However, these augmentations do require an expensive network of reference receivers and real-time broadcast to the airborne user. To overcome, the ABAS system integrates on-board information provided by an INS so as to enhance the performance of the navigation system. In that scheme, INS is coupled with a GPS receiver in a GPS/baro-INS hybridization solution that is already performed on current commercial aircraft. This solution allows reaching better performance in terms of accuracy, integrity, availability and continuity than the two separated solutions. However the most stringent requirements for precision approaches or automatic landings cannot be fulfilled with the current hybridization. The main idea of this PhD study is then to extend the hybridization process by including other sensors already available on commercial aircraft or not and, to assess the performance reached by a global hybridization architecture. It aims at providing most of the navigation parameters in all operations with the required level of performance. The operations targeted by this hybridization are precision approaches, with a particular focus on CAT III precision approach and roll out on the runway. The study of video sensor has been particularly focused on in the thesis. Indeed video based navigation is a complete autonomous navigation opportunity only based on sensors that provide information from the dynamic of the vehicle and from the observation of the scenery. Moreover, from a possible compensation of any loss or degradation of a navigation system to the improvement of the navigation solution during the most critical operations, the interests of video are numerous

Low cost inertial-based localization system for a service robot

Dissertation presented at Faculty of Sciences and Technology of the New University of Lisbon to attain the Master degree in Electrical and Computer Science EngineeringThe knowledge of a robot’s location it’s fundamental for most part of service robots. The success of tasks such as mapping and planning depend on a good robot’s position knowledge. The main goal of this dissertation is to present a solution that provides a estimation of the robot’s location. This is, a tracking system that can run either inside buildings or outside them, not taking into account just structured environments. Therefore, the localization system takes into account only measurements relative. In the presented solution is used an AHRS device and digital encoders placed on wheels to make a estimation of robot’s position. It also relies on the use of Kalman Filter to integrate sensorial information and deal with estimate errors. The developed system was testes in real environments through its integration on real robot. The results revealed that is not possible to attain a good position estimation using only low-cost inertial sensors. Thus, is required the integration of more sensorial information, through absolute or relative measurements technologies, to provide a more accurate position estimation

Data-Driven Denoising of Stationary Accelerometer Signals

Modern navigation solutions are largely dependent on the performances of the standalone inertial sensors, especially at times when no external sources are available. During these outages, the inertial navigation solution is likely to degrade over time due to instrumental noises sources, particularly when using consumer low-cost inertial sensors. Conventionally, model-based estimation algorithms are employed to reduce noise levels and enhance meaningful information, thus improving the navigation solution directly. However, guaranteeing their optimality often proves to be challenging as sensors performance differ in manufacturing quality, process noise modeling, and calibration precision. In the literature, most inertial denoising models are model-based when recently several data-driven approaches were suggested primarily for gyroscope measurements denoising. Data-driven approaches for accelerometer denoising task are more challenging due to the unknown gravity projection on the accelerometer axes. To fill this gap, we propose several learning-based approaches and compare their performances with prominent denoising algorithms, in terms of pure noise removal, followed by stationary coarse alignment procedure. Based on the benchmarking results, obtained in field experiments, we show that: (i) learning-based models perform better than traditional signal processing filtering; (ii) non-parametric kNN algorithm outperforms all state of the art deep learning models examined in this study; (iii) denoising can be fruitful for pure inertial signal reconstruction, but moreover for navigation-related tasks, as both errors are shown to be reduced up to one order of magnitude.Comment: 10 pages, 15 figures, 8 table

Implementation of a Low Cost Micro Electro Mechanical Systems Inertial Navigation Solution

The purpose of this study is to develop a low cost navigation solution for General Aviation (GA) aircraft and flight testing aircraft. This low cost solution uses a Global Positioning System (GPS) coupled with an Inertial Navigation Solution (INS) to provide a more accurate, stable navigation solution that can be acceptable for aircraft navigation. Lab VIEW was utilized to perform data acquisition and computation. LabVIEW was the backbone software to perform real time computation. MATLAB ® Simulink ® was used to plot graphs for final analysis of the code. A software Kalman Filter was utilized to provide signal stability and signal continuity. This study will be utilized as a preliminary step towards the development of inexpensive alternate methods of aircraft navigation for GA and flight testing