Existing navigation systems are very appropriate
for car navigation, but lack support for convenient
pedestrian navigation and cannot be used indoors due to
GPS limitations. In addition, the creation and the
maintenance of the required models are costly and time
consuming, and are usually based on proprietary data
structures. In this paper we describe a navigation system
based on a human inspired symbolic space model. We argue
that symbolic space models are much easier to create and to
maintain, and that they can support routing applications
based on self-locating through the recognition of nearby
features. Our symbolic space model is supported by a
federation of servers where the spatial descriptions are
stored, and which provide interfaces for feeding and
querying the model. Local models residing in different
servers may be connected between them, thus contributing
to the system scalability.; Fundação para a Ciência e a Tecnologia (FCT)
Nowadays there is an increase of location-aware
mobile applications. However, these applications only retrieve
location with a mobile device’s GPS chip. This means that in
indoor or in more dense environments these applications don’t
work properly. To provide location information everywhere a
pedestrian Inertial Navigation System (INS) is typically used, but
these systems can have a large estimation error since, in order
to turn the system wearable, they use low-cost and low-power
sensors. In this work a pedestrian INS is proposed, where force
sensors were included to combine with the accelerometer data in
order to have a better detection of the stance phase of the human
gait cycle, which leads to improvements in location estimation.
Besides sensor fusion an information fusion architecture is proposed,
based on the information from GPS and several inertial
units placed on the pedestrian body, that will be used to learn
the pedestrian gait behavior to correct, in real-time, the inertial
sensors errors, thus improving location estimation.; This work is funded by National Funds through the
FCT - Fundação para a Ciência e a Tecnologia (Portuguese
Foundation for Science and Technology) within project PEst-
OE/EEI/UI0752/2014. The work of Ricardo Anacleto is supported
by a doctoral grant by FCT SFRH/BD/70248/2010.
A single low cost inertial measurement unit (IMU) is often used in conjunction with GPS to increase the accuracy and improve the availability of the navigation solution for a pedestrian navigation system. This paper develops several fusion algorithms for using multiple IMUs to enhance performance. In particular, this research seeks to understand the benefits and detriments of each fusion method in the context of pedestrian navigation. Three fusion methods are proposed. First, all raw IMU measurements are mapped onto a common frame (i.e., a virtual frame) and processed in a typical combined GPS-IMU Kalman filter. Second, a large stacked filter is constructed of several IMUs. This filter construction allows for relative information between the IMUs to be used as updates. Third, a federated filter is used to process each IMU as a local filter. The output of each local filter is shared with a master filter, which in turn, shares information back with the local filters. The construction of each filter is discussed and improvements are made to the virtual IMU (VIMU) architecture, which is the most commonly used architecture in the literature. Since accuracy and availability are the most important characteristics of a pedestrian navigation system...
In an inertial sensor-based pedestrian navigation system, the position is estimated by double integrating external acceleration. A new algorithm is proposed to reduce z axis position (height) error. When a foot is on the ground, a foot angle is estimated using accelerometer output. Using a foot angle, the inclination angle of a road is estimated. Using this road inclination angle, height difference of one walking step is estimated and this estimation is used to reduce height error. Through walking experiments on roads with different inclination angles, the usefulness of the proposed algorithm is verified.
Many solutions have been proposed for indoor pedestrian navigation. Some rely on pre-installed sensor networks, which offer good accuracy but are limited to areas that have been prepared for that purpose, thus requiring an expensive and possibly time-consuming process. Such methods are therefore inappropriate for navigation in emergency situations since the power supply may be disturbed. Other types of solutions track the user without requiring a prepared environment. However, they may have low accuracy. Offline tracking has been proposed to increase accuracy, however this prevents users from knowing their position in real time. This paper describes a real time indoor navigation system that does not require prepared building environments and provides tracking accuracy superior to previously described tracking methods. The system uses a combination of four techniques: foot-mounted IMU (Inertial Motion Unit), ultrasonic ranging, particle filtering and model-based navigation. The very purpose of the project is to combine these four well-known techniques in a novel way to provide better indoor tracking results for pedestrians.
Most portable systems like smart-phones are equipped with low cost consumer grade sensors, making them useful as Pedestrian Navigation Systems (PNS). Measurements of these sensors are severely contaminated by errors caused due to instrumentation and environmental issues rendering the unaided navigation solution with these sensors of limited use. The overall navigation error budget associated with pedestrian navigation can be categorized into position/displacement errors and attitude/orientation errors. Most of the research is conducted for tackling and reducing the displacement errors, which either utilize Pedestrian Dead Reckoning (PDR) or special constraints like Zero velocity UPdaTes (ZUPT) and Zero Angular Rate Updates (ZARU). This article targets the orientation/attitude errors encountered in pedestrian navigation and develops a novel sensor fusion technique to utilize the Earth’s magnetic field, even perturbed, for attitude and rate gyroscope error estimation in pedestrian navigation environments where it is assumed that Global Navigation Satellite System (GNSS) navigation is denied. As the Earth’s magnetic field undergoes severe degradations in pedestrian navigation environments, a novel Quasi-Static magnetic Field (QSF) based attitude and angular rate error estimation technique is developed to effectively use magnetic measurements in highly perturbed environments. The QSF scheme is then used for generating the desired measurements for the proposed Extended Kalman Filter (EKF) based attitude estimator. Results indicate that the QSF measurements are capable of effectively estimating attitude and gyroscope errors...
The integration of Global Navigation Satellite Systems (GNSS) with Inertial Navigation Systems (INS) has been very actively researched for many years due to the complementary nature of the two systems. In particular, during the last few years the integration with micro-electromechanical system (MEMS) inertial measurement units (IMUs) has been investigated. In fact, recent advances in MEMS technology have made possible the development of a new generation of low cost inertial sensors characterized by small size and light weight, which represents an attractive option for mass-market applications such as vehicular and pedestrian navigation. However, whereas there has been much interest in the integration of GPS with a MEMS-based INS, few research studies have been conducted on expanding this application to the revitalized GLONASS system. This paper looks at the benefits of adding GLONASS to existing GPS/INS(MEMS) systems using loose and tight integration strategies. The relative benefits of various constraints are also assessed. Results show that when satellite visibility is poor (approximately 50% solution availability) the benefits of GLONASS are only seen with tight integration algorithms. For more benign environments, a loosely coupled GPS/GLONASS/INS system offers performance comparable to that of a tightly coupled GPS/INS system...
Navigation and location technologies are continually advancing, allowing ever higher accuracies and operation under ever more challenging conditions. The development of such technologies requires the rapid evaluation of a large number of sensors and related utilization strategies. The integration of Global Navigation Satellite Systems (GNSSs) such as the Global Positioning System (GPS) with accelerometers, gyros, barometers, magnetometers and other sensors is allowing for novel applications, but is hindered by the difficulties to test and compare integrated solutions using multiple sensor sets. In order to achieve compatibility and flexibility in terms of multiple sensors, an advanced adaptable platform is required. This paper describes the design and testing of the NavCube, a multi-sensor navigation, location and timing platform. The system provides a research tool for pedestrian navigation, location and body motion analysis in an unobtrusive form factor that enables in situ data collections with minimal gait and posture impact. Testing and examples of applications of the NavCube are provided.
We present a waist-worn personal navigation system based on inertial measurement units. The device makes use of the human bipedal pattern to reduce position errors. We describe improved algorithms, based on detailed description of the heel strike biomechanics and its translation to accelerations of the body waist to estimate the periods of zero velocity, the step length, and the heading estimation. The experimental results show that we are able to support pedestrian navigation with the high-resolution positioning required for most applications.
Dead-reckoning (DR) algorithms, which use self-contained inertial sensors combined with gait analysis, have proven to be effective for pedestrian navigation purposes. In such DR systems, the primary error is often due to accumulated heading drifts. By tightly integrating global navigation satellite system (GNSS) Doppler measurements with DR, such accumulated heading errors can usually be accurately compensated. Under weak signal conditions, high sensitivity GNSS (HSGNSS) receivers with block processing techniques are often used, however, the Doppler quality of such receivers is relatively poor due to multipath, fading and signal attenuation. This often limits the benefits of integrating HSGNSS Doppler with DR. This paper investigates the benefits of using Doppler measurements from a novel direct vector HSGNSS receiver with pedestrian dead-reckoning (PDR) for indoor navigation. An indoor signal and multipath model is introduced which explains how conventional HSGNSS Doppler measurements are affected by indoor multipath. Velocity and Doppler estimated by using direct vector receivers are introduced and discussed. Real experimental data is processed and analyzed to assess the veracity of proposed method. It is shown when integrating HSGNSS Doppler with PDR algorithm...
We propose a novel hybrid inertial sensors-based indoor pedestrian dead reckoning system, aided by computer vision-derived position measurements. In contrast to prior vision-based or vision-aided solutions, where environmental markers were used—either deployed in known positions or extracted directly from it—we use a shoe-fixed marker, which serves as positional reference to an opposite shoe-mounted camera during foot swing, making our system self-contained. Position measurements can be therefore more reliably fed to a complementary unscented Kalman filter, enhancing the accuracy of the estimated travelled path for 78%, compared to using solely zero velocities as pseudo-measurements.
Inertial navigation based on micro-electromechanical system (MEMS) inertial measurement units (IMUs) has attracted numerous researchers due to its high reliability and independence. The heading estimation, as one of the most important parts of inertial navigation, has been a research focus in this field. Heading estimation using magnetometers is perturbed by magnetic disturbances, such as indoor concrete structures and electronic equipment. The MEMS gyroscope is also used for heading estimation. However, the accuracy of gyroscope is unreliable with time. In this paper, a wearable multi-sensor system has been designed to obtain the high-accuracy indoor heading estimation, according to a quaternion-based unscented Kalman filter (UKF) algorithm. The proposed multi-sensor system including one three-axis accelerometer, three single-axis gyroscopes, one three-axis magnetometer and one microprocessor minimizes the size and cost. The wearable multi-sensor system was fixed on waist of pedestrian and the quadrotor unmanned aerial vehicle (UAV) for heading estimation experiments in our college building. The results show that the mean heading estimation errors are less 10° and 5° to multi-sensor system fixed on waist of pedestrian and the quadrotor UAV...
In recent years, there is a mounting obligation for indoor location based services Comparable to outdoor location based services . Indoor guidance systems provide ample utilities to a user explicitly huge complex at shopping malls , hospitals and at vast libraries for any directed assistance. Pedestrian navigation is one such promising indoor location based service. Localization remains a basis for all location based services . Although, few pedestrian -based indoor localization are systems available in market , they lack either one of the attributes as such accuracy, reliability, scalability and / or expensive. GPS is not meant for indoors and even if so used at indoors , its relatively weak signal still stay a hurdle for any indoor location based services .
In this dissertation , the aim is to build an efficient and precise indoor localization approach that can be implemented for most of the large indoor environments. The projected approach in this study incomparable to GPS that works outdoors will, remain eminently than other available indoor localization based approaches. All prerequisite for efficient localization such as accuracy, reliability, scalability, flexibility, availability, cost efficiency , minimum latency and robustness were evaluated reconstructed for this approach that is herewith demonstrated in my dissertation.
The dissertation is structured as various chapters . Each of the chapter in this dissertation portrays novel methods utilizing major sensor technologies such as Bluetooth...
Nowadays the incredible grow of mobile devices market led
to the need for location-aware applications. However, sometimes person
location is di cult to obtain, since most of these devices only have a GPS
(Global Positioning System) chip to retrieve location. In order to sup-
press this limitation and to provide location everywhere (even where a
structured environment doesn't exist) a wearable inertial navigation sys-
tem is proposed, which is a convenient way to track people in situations
where other localization systems fail. The system combines pedestrian
dead reckoning with GPS, using widely available, low-cost and low-power
hardware components. The system innovation is the information fusion
and the use of probabilistic methods to learn persons gait behavior to
correct, in real-time, the drift errors given by the sensors.; This work is part-funded by ERDF - European Regional Development Fund through
the COMPETE Programme (operational programme for competitiveness) and by
National Funds through the FCT Fundao para a Cincia e a Tecnologia (Portuguese
Foundation for Science and Technology) within project FCOMP-01-0124-FEDER-
028980 (PTDC/EEI-SII/1386/2012). Ricardo also acknowledge FCT for the support
of his work through the PhD grant (SFRH/DB/70248/2010).
Nowadays there is an increase of location-aware mobile applications. However, these applications only retrieve location with a mobile device's GPS chip. This means that in indoor or in more dense environments these applications don't work properly. To provide location information everywhere a pedestrian Inertial Navigation System (INS) is typically used, but these systems can have a large estimation error since, in order to turn the system wearable, they use low-cost and low-power sensors. In this work a pedestrian INS is proposed, where force sensors were included to combine with the accelerometer data in order to have a better detection of the stance phase of the human gait cycle, which leads to improvements in location estimation. Besides sensor fusion an information fusion architecture is proposed, based on the information from GPS and several inertial units placed on the pedestrian body, that will be used to learn the pedestrian gait behavior to correct, in real-time, the inertial sensors errors, thus improving location estimation.
Heading estimation is a central problem for indoor pedestrian navigation using the pervasively available smartphone. For smartphones placed in a pocket, one of the most popular device positions, the essential challenges in heading estimation are the changing device coordinate system and the severe indoor magnetic perturbations. To address these challenges, we propose a novel heading estimation approach based on a rotation matrix and principal component analysis (PCA). Firstly, through a related rotation matrix, we project the acceleration signals into a reference coordinate system (RCS), where a more accurate estimation of the horizontal plane of the acceleration signal is obtained. Then, we utilize PCA over the horizontal plane of acceleration signals for local walking direction extraction. Finally, in order to translate the local walking direction into the global one, we develop a calibration process without requiring noisy compass readings. Besides, a turn detection algorithm is proposed to improve the heading estimation accuracy. Experimental results show that our approach outperforms the traditional uDirect and PCA-based approaches in terms of accuracy and feasibility.
Inertial navigation systems use dead-reckoning to estimate the pedestrian's position. There are two types of pedestrian dead-reckoning, the strapdown algorithm and the step-and-heading approach. Unlike the strapdown algorithm, which consists of the double integration of the three orthogonal accelerometer readings, the step-and-heading approach lacks the vertical displacement estimation. We propose the first step-and-heading approach based on unaided inertial data solving 3D positioning. We present a step detector for steps up and down and a novel vertical displacement estimator. Our navigation system uses the sensor introduced in the front pocket of the trousers, a likely location of a smartphone. The proposed algorithms are based on the opening angle of the leg or pitch angle. We analyzed our step detector and compared it with the state-of-the-art, as well as our already proposed step length estimator. Lastly, we assessed our vertical displacement estimator in a real-world scenario. We found that our algorithms outperform the literature step and heading algorithms and solve 3D positioning using unaided inertial data. Additionally, we found that with the pitch angle, five activities are distinguishable: standing, sitting, walking, walking up stairs and walking down stairs. This information complements the pedestrian location and is of interest for applications...
This paper presents a step count algorithm designed to work in real-time using low computational power. This proposal is our first step for the development of an indoor navigation system, based on Pedestrian Dead Reckoning (PDR). We present two approaches to solve this problem and compare them based in their error on step counting, as well as, the capability of their use in a real time system.
Inertial navigation systems for pedestrians are infrastructure-less and can
achieve sub-meter accuracy in the short/medium period. However, when low-cost
inertial measurement units (IMU) are employed for their implementation, they
suffer from a slowly growing drift between the true pedestrian position and the
corresponding estimated position. In this paper we illustrate a novel solution
to mitigate such a drift by: a) using only accelerometer and gyroscope
measurements (no magnetometers required); b) including the sensor error model
parameters in the state vector of an extended Kalman filter; c) adopting a
novel soft heuristic for foot stance detection and for zero-velocity updates.
Experimental results evidence that our inertial-only navigation system can
achieve similar or better performance with respect to pedestrian dead-reckoning
systems presented in related studies, although the adopted IMU is less accurate
than more expensive counterparts.
Most of the developed pedestrian navigators rely on the use of satellite positioning (GNSS), sometimes also in combination with other sensors and positioning methods. In the project “Ubiquitous Cartography for Pedestrian Navigation” (UCPNAVI) we have integrated active Radio Frequency Identification (RFID) in combination with GNSS and Inertial Navigation Systems (INS) for continuous positioning. RFID can be employed in areas where no satellite positioning is possible due to obstructions, e.g. in urban canyons and indoor environments. In RFID positioning the location estimation is based on Received Signal Strength Indication (RSSI) which is a measurement of the power present in a received radio signal. The receiver can compute its position using various methods based on RSSI. In total, three different methods have been developed and investigated, i.e., cell-based positioning, trilateration and RFID location fingerprinting. These methods can be employed depending on the density of the RFID tags in the surrounding environment providing different levels of positioning accuracies. By integrating the three methods for positioning into an intelligent software package and developing a knowledge-based system it is possible to determine the pedestrian position automatically and ubiquitously. The concept of the intelligent software package is presented and described in the paper. For improvement of the positioning accuracy of cell-based positioning a modification has been developed...