An Intelligent Navigation Solution for Land Mobile Location-Based Services

The increasing availability of small, low-cost GPS receivers has established a firm growth in the production of Location-Based Services (LBS). LBS, such as in-car navigation systems, are not necessarily reliant on high accuracy but a continuous positioning service. When available, the accuracy provided by the standard positioning service (SPS) of 30 metres, 95% of the time is often acceptable. The reality is, however, that GPS does not work in all situations, and it is therefore common to integrate GPS with additional sensors. The use of low-cost inertial sensors alone during GPS signal outage is severely restricted due to the accumulation of errors that is inherent with such dead reckoning (DR) systems. Through the integration of spatial information with real-time positioning sensors, intelligence can be added to the land mobile navigation solution. The information contained within a Geographical Information System (GIS) provides additional observations that can be used to improve the navigation result. With this approach, the solution is not dependent on the performance capabilities of the navigation sensors alone. This enables the use of lower accuracy navigation devices, allowing low-cost systems to provide a sustained, viable navigation solution despite long-term GPS outages. Practical results are presented comparing solutions obtained from a hand-held GPS receiver to a gyroscope and odometer.

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S-PDR: SBAUPT-Based Pedestrian Dead Reckoning Algorithm for Free-Moving Handheld Devices

Geomatics ◽

10.3390/geomatics1020010 ◽

2021 ◽

Vol 1 (2) ◽

pp. 148-176

Author(s):

Maan Khedr ◽

Naser El-Sheimy

Keyword(s):

Smart Cities ◽

Dead Reckoning ◽

Human Motion ◽

Indoor Navigation ◽

Location Based Services ◽

Navigation Systems ◽

Short Term ◽

Navigation Solution ◽

Correction Technique ◽

Pedestrian Dead Reckoning

Mobile location-based services (MLBS) are attracting attention for their potential public and personal use for a variety of applications such as location-based advertisement, smart shopping, smart cities, health applications, emergency response, and even gaming. Many of these applications rely on Inertial Navigation Systems (INS) due to the degraded GNSS services indoors. INS-based MLBS using smartphones is hindered by the quality of the MEMS sensors provided in smartphones which suffer from high noise and errors resulting in high drift in the navigation solution rapidly. Pedestrian dead reckoning (PDR) is an INS-based navigation technique that exploits human motion to reduce navigation solution errors, but the errors cannot be eliminated without aid from other techniques. The purpose of this study is to enhance and extend the short-term reliability of PDR systems for smartphones as a standalone system through an enhanced step detection algorithm, a periodic attitude correction technique, and a novel PCA-based motion direction estimation technique. Testing shows that the developed system (S-PDR) provides a reliable short-term navigation solution with a final positioning error that is up to 6 m after 3 min runtime. These results were compared to a PDR solution using an Xsens IMU which is known to be a high grade MEMS IMU and was found to be worse than S-PDR. The findings show that S-PDR can be used to aid GNSS in challenging environments and can be a viable option for short-term indoor navigation until aiding is provided by alternative means. Furthermore, the extended reliable solution of S-PDR can help reduce the operational complexity of aiding navigation systems such as RF-based indoor navigation and magnetic map matching as it reduces the frequency by which these aiding techniques are required and applied.

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MLCA—A Machine Learning Framework for INS Coarse Alignment

Sensors ◽

10.3390/s20236959 ◽

2020 ◽

Vol 20 (23) ◽

pp. 6959

Author(s):

Idan Zak ◽

Reuven Katz ◽

Itzik Klein

Keyword(s):

Machine Learning ◽

Field Experiments ◽

Inertial Sensors ◽

Initial Conditions ◽

Low Cost ◽

Navigation Systems ◽

Learning Approaches ◽

Alignment Procedure ◽

Navigation Solution ◽

Learning Framework

Inertial navigation systems provides the platform’s position, velocity, and attitude during its operation. As a dead-reckoning system, it requires initial conditions to calculate the navigation solution. While initial position and velocity vectors are provided by external means, the initial attitude can be determined using the system’s inertial sensors in a process known as coarse alignment. When considering low-cost inertial sensors, only the initial roll and pitch angles can be determined using the accelerometers measurements. The accuracy, as well as time required for the for the coarse alignment process are critical for the navigation solution accuracy, particularly for pure-inertial scenarios, because of the navigation solution drift. In this paper, a machine learning framework for the stationary coarse alignment stage is proposed. To that end, classical machine learning approaches are used in a two-stage approach to regress the roll and pitch angles. Alignment results obtained both in simulations and field experiments, using a smartphone, shows the benefits of using the proposed approach instead of the commonly used analytical coarse alignment procedure.

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A Feasibility Study of Machine Learning Based Coarse Alignment

Proceedings ◽

10.3390/ecsa-5-05735 ◽

2018 ◽

Vol 4 (1) ◽

pp. 50 ◽

Cited By ~ 1

Author(s):

Idan Zak ◽

Itzik Klein ◽

Reuven Katz

Keyword(s):

Machine Learning ◽

Feasibility Study ◽

Inertial Sensors ◽

Low Cost ◽

Navigation Systems ◽

Alignment Procedure ◽

Navigation Solution ◽

Alignment Process ◽

Boosting Method ◽

Time Required

Inertial navigation systems (INSs) require an initial attitude before its operation. To that end, the coarse alignment process is applied using inertial sensors readings. For low-cost inertial sensors, only the accelerometers readings are processed to yield the initial roll and pitch angles. The accuracy of the coarse alignment procedure is vitally important for the navigation solution accuracy due to the navigation solution drift accumulating over time. In this paper, we propose using machine learning (ML) approaches, instead of traditional approaches, to conduct the coarse alignment procedure. To that end, a new methodology for the alignment process is proposed, based on state-of-the-art ML algorithms such as random forest (RF) and the more advanced boosting method of gradient tree XGBoost. Results from a simulated alignment of stationary INS scenarios are presented accompanied by a feasibility study. ML results are compared with the traditional coarse alignment methods in terms of time to convergence and accuracy performance. When using the proposed approach, with the examined scenarios, results show a significant improvement of the accuracy and time required for the alignment process.

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Constructing Floor Plan through Smoke Using Ultra Wideband Radar

Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies ◽

10.1145/3494977 ◽

2021 ◽

Vol 5 (4) ◽

pp. 1-29

Author(s):

Weiyan Chen ◽

Fusang Zhang ◽

Tao Gu ◽

Kexing Zhou ◽

Zixuan Huo ◽

...

Keyword(s):

Spatial Information ◽

Low Cost ◽

Indoor Navigation ◽

Location Based Services ◽

Ultra Wideband ◽

Indoor Environments ◽

Floor Plan ◽

Construction Methods ◽

Median Error ◽

Indoor Scenarios

Floor plan construction has been one of the key techniques in many important applications such as indoor navigation, location-based services, and emergency rescue. Existing floor plan construction methods require expensive dedicated hardware (e.g., Lidar or depth camera), and may not work in low-visibility environments (e.g., smoke, fog or dust). In this paper, we develop a low-cost Ultra Wideband (UWB)-based system (named UWBMap) that is mounted on a mobile robot platform to construct floor plan through smoke. UWBMap leverages on low-cost and off-the-shelf UWB radar, and it is able to construct an indoor map with an accuracy comparable to Lidar (i.e., the state-of-the-art). The underpinning technique is to take advantage of the mobility of radar to form virtual antennas and gather spatial information of a target. UWBMap also eliminates both robot motion noise and environmental noise to enhance weak reflection from small objects for the robust construction process. In addition, we overcome the limited view of single radar by combining multi-view from multiple radars. Extensive experiments in different indoor environments show that UWBMap achieves a map construction with a median error of 11 cm and a 90-percentile error of 26 cm, and it operates effectively in indoor scenarios with glass wall and dense smoke.

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Trilateration approaches for seamless out-/indoor GNSS and Wi-Fi smartphone positioning

Journal of Applied Geodesy ◽

10.1515/jag-2018-0022 ◽

2019 ◽

Vol 13 (1) ◽

pp. 47-61

Author(s):

Guenther Retscher ◽

Jonathan Kleine ◽

Lisa Whitemore

Keyword(s):

Inertial Sensors ◽

Low Cost ◽

Ground Truth ◽

Mobile User ◽

Location Based Services ◽

Raspberry Pi ◽

Indoor Environments ◽

Point Location ◽

Point Determination ◽

The Relationship

Abstract More and more sensors and receivers are found nowadays in smartphones which can enable and improve positioning for Location-based Services and other navigation applications. Apart from inertial sensors, such as accelerometers, gyroscope and magnetometer, receivers for Wireless Fidelity (Wi-Fi) and GNSS signals can be employed for positioning of a mobile user. In this study, three trilateration methods for Wi-Fi positioning are investigated whereby the influence of the derivation of the relationship between the received signal strength (RSS) and the range to an Access Points (AP) are analyzed. The first approach is a straightforward resection for point determination and the second is based on the calculation of the center of gravity in a triangle of APs while weighting the received RSS. In the third method a differential approach is employed where as in Differential GNSS (DGNSS) corrections are derived and applied to the raw RSS measurements. In this Differential Wi-Fi (DWi-Fi) method, reference stations realized by low-cost Raspberry Pi units are used to model temporal RSS variations. In the experiments in this study two different indoor environments are used, one in a laboratory and the second in the entrance of an office building. The results of the second and third approach show position deviations from the ground truth of around 2 m in dependence of the geometrical point location. Furthermore, the transition between GNSS positioning outdoors and Wi-Fi localization indoors in the entrance area of the building is studied.

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Trilateration Approaches for Indoor Wi-Fi Positioning

E3S Web of Conferences ◽

10.1051/e3sconf/20199402002 ◽

2019 ◽

Vol 94 ◽

pp. 02002

Author(s):

Guenther Retscher ◽

Jonathan Kleine ◽

Lisa Whitemore

Keyword(s):

Inertial Sensors ◽

Low Cost ◽

Ground Truth ◽

Center Of Gravity ◽

Location Based Services ◽

Raspberry Pi ◽

Point Location ◽

Differential Approach ◽

Time Modeling ◽

Gnss Receivers

In smartphones several sensors and receivers are embedded which enable positioning in Location-based Services and other navigation applications. They include GNSS receivers and Wireless Fidelity (Wi-Fi) cards as well as inertial sensors, such as accelerometers, gyroscope and magnetometer. In this paper, indoor Wi-Fi positioning is studied based on trilateration. Three methods are investigated which are a resection, a calculation of the center of gravity point and a differential approach. The first approach is a commonly employed resection using the ranges to the Wi-Fi Access Points (APs) as radii and intersect the circles around the APs. In the second method, the center of gravity in a triangle of APs is calculated with weighting of the received signal strength (RSS) of the Wi-Fi signals. The third approach is developed by analogy to Differential GNSS (DGNSS) and therefore termed Differential Wi-Fi (DWi-Fi). Its advantage is that a real-time modeling of the temporal RSS variations and fluctuations is possible. For that purpose, reference stations realized by low-cost Raspberry Pi units are deployed which serve at the same time as APs. The experiments conducted in a laboratory and entrance of an office building showed that position deviations from the ground truth of around 2 m are achievable with the second and third method. Thereby the positioning accuracies depend mainly on the geometrical point location in the triangle of APs and reference stations and the RSS scan duration.

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Feasibility Study of Multi Inertial Measurement Unit

Proceedings ◽

10.3390/ecsa-6-06582 ◽

2019 ◽

Vol 42 (1) ◽

pp. 74 ◽

Cited By ~ 1

Author(s):

Ariel Larey ◽

Eliel Aknin ◽

Itzik Klein

Keyword(s):

Inertial Measurement Unit ◽

Inertial Sensors ◽

Low Cost ◽

Software Tool ◽

Experimental Results ◽

Measurement Unit ◽

Outlier Rejection ◽

Inertial Measurement ◽

Navigation Solution ◽

Sensory Data

An inertial measurement unit (IMU) typically has three accelerometers and three gyroscopes. The output of those inertial sensors is used by an inertial navigation system to calculate the navigation solution–position, velocity and attitude. Since the sensor measurements contain noise, the navigation solution drifts over time. When considering low cost sensors, multiple IMUs can be used to improve the performance of a single unit. In this paper, we describe our designed 32 multi-IMU (MIMU) architecture and present experimental results using this system. To analyze the sensory data, a dedicated software tool, capable of addressing MIMUs inputs, was developed. Using the MIMU hardware and software tool we examined and evaluated the MIMUs for: (1) navigation solution accuracy (2) sensor outlier rejection (3) stationary calibration performance (4) coarse alignment accuracy and (5) the effect of different MIMUs locations in the architecture. Our experimental results show that 32 IMUs obtained better performance than a single IMU for all testcases examined. In addition, we show that performance was improved gradually as the number of IMUs was increased in the architecture.

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Calibration of a Triaxial, Consumer-grade Magnetometer via an Extended Two-step Methodology

10.48011/asba.v2i1.1571 ◽

2020 ◽

Author(s):

Rogério P. Menezes Filho ◽

Felipe O. Silva ◽

Leonardo A. Vieira ◽

Lucas P. S. Paiva ◽

Gustavo S. Carvalho

Keyword(s):

Law Enforcement ◽

Hardware Implementation ◽

Inertial Sensors ◽

Film Industry ◽

Low Cost ◽

External Information ◽

Navigation Systems ◽

Smartphone Applications ◽

Pros And Cons ◽

Position And Orientation

Humans have always had the necessity of estimating their location in space for various reasons, e.g. hunting, traveling, sailing, battling, etc. Today, many other areas also demand that information, such as aviation, agriculture, multiple smartphone applications, law enforcement, and even film industry, to mention but a few. Estimating position and orientation is known as navigation, and the means to achieve it are called navigation systems. Each approach has its pros and cons, but sometimes it is possible to combine them into an improved architecture. For instance, inertial sensors (i.e. accelerometers and gyroscopes) can be integrated with magnetometers, producing an Attitude and Heading Reference System (AHRS); this process is referred to as sensor fusion. However, before sensors can be used to produce the navigation solution, calibration is often necessary, especially for low-cost devices. In this study,we perform the calibration of a triaxial consumer-grade magnetometer via an extended two-step methodology, correct small mistakes present in the original paper, and evaluate the technique in a restricted motion scenario. This technique can be implemented in-field, simply by rotating the sensors to multiple orientations; the only external information necessary is the local Earth's magnetic field density, easily estimated through reliable models. The error parameters, i.e. biases, scale factors, and misalignments, are indirectly estimated via a least squares algorithm. The calibration is first performed through software simulation, followed by hardware implementation to validate the results.

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Specialized Algorithm for Navigation of a Micro Hopping Air Vehicle Using Only Inertial Sensors

Journal of Dynamic Systems Measurement and Control ◽

10.1115/1.4007975 ◽

2012 ◽

Vol 135 (2) ◽

Author(s):

Edward Scheuermann ◽

Mark Costello

Keyword(s):

Data Stream ◽

Inertial Sensors ◽

Micro Air Vehicles ◽

Positional Information ◽

Estimation Accuracy ◽

Measurement Unit ◽

Navigation Systems ◽

Navigation Solution ◽

Air Vehicle ◽

Vehicle Position

The need for accurate and reliable navigation techniques for micro air vehicles plays an important part in enabling autonomous operation. Traditional navigation systems typically rely on periodic global positioning system updates and provide little benefit when operating indoors or in other similarly shielded environments. Moreover, direct integration of the onboard inertial measurement unit data stream often results in substantial drift errors yielding virtually unusable positional information. This paper presents a new strategy for obtaining an accurate navigation solution for the special case of a micro hopping air vehicle, beginning from some known location and heading, using only one triaxial accelerometer and one triaxial gyroscope. Utilizing the unique dynamics of the hopping vehicle, a piece-wise navigation solution is constructed by selectively integrating the inertial data stream for only those short periods of time while the vehicle is airborne. Interhop data post processing and sensor bias recalibration are also used to further improve estimation accuracy. To assess the performance of the proposed algorithm, a series of tests were conducted in which the estimated vehicle position following a sequence of 10 consecutive hops was compared with measurements from an optical motion-capture system. On average, the final estimated vehicle position was within 0.70 m or just over 6% from its actual location based on a total traveled distance of approximately 11 m.

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