Browsing by Author "Noureldin, Aboelmagd"
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Item Open Access A Methodology for Autonomous Navigation and Mapping in an Unknown Unstructured Dynamic Indoor Environment(2017) Mohamed, Haytham Alaa Eldin Abdalla; El-Sheimy, Naser; Sesay, Abu-Bakarr B; Elhabiby, Mohamed; Noureldin, Aboelmagd; Costa Sousa, Mario; El-Rabbany, AhmedUnmanned aerial vehicles (UAVs) became an effective technology for indoor search and rescue operations, providing real-time mapping of the environment, locating victims, and determining the hard-hit areas after a natural disaster. Typically, most of the indoor missions’ environments could be unknown, unstructured, and/or dynamic. Therefore, navigation of UAVs in such environments is addressed by Simultaneous Localization and Mapping approach (SLAM) in either local or global scan matching approaches. SLAM approaches that utilize laser rangefinders depend on a scan matching method of the successive scans. The local approaches suffer from high time consumption due to iterative fashion of the scan matching method. Moreover, point-to-point scan matching is prone to bad data association process. Thus, a preceding initialization step is proposed before the local approach. This step aims to increase the convergence probability and to decrease the time consumption by limiting the number of iterations needed to reach convergence. However, the local approach still suffers from accumulated errors. Hector SLAM algorithm, as a global approach, suffers from getting trapped in local minima because of the employed gradient ascent. Hence, the multi-resolution map representation is utilized to avoid getting trapped in local minima. However, this approach increases the time consumption and the memory requirements of the process. Thus, a preceding initialization step is proposed before the Hector SLAM algorithm. This step aims to reduce the process time consumption and decrease the multi-resolution map representation into a single level with small grid cell size. However, the scan matching process of the Hector SLAM algorithm still suffers from accumulated errors. Therefore, a low-cost novel method for 2D real-time laser scan matching based on reference key frame is proposed. The proposed method is a hybrid scan matching technique comprised of feature-to-feature and point-to-point approaches, using single laser scan rangefinder, and optical flow sensors. Unlike the local and global approaches, the proposed algorithm aims at mitigating errors accumulation using the key frame technique, which is inspired from video streaming broadcast process. The SLAM approach is implemented using a UAV. In this scenario, the UAV can translate and rotate around all its axes. Consequently, navigating in 3D environments often requires 3D representation of the environments which usually suffers from memory and computational costs. Thus, an efficient 3D SLAM approach is proposed using multiple 2D point cloud slices. Furthermore, for autonomous exploration, the UAV should be able to mimic humans and take decisions according to the surrounding situations. Hence, the vehicle must be able to detect a proper destination and generate an appropriate path to that destination. Since the time constraint is a key factor for most indoor search and rescue operations, an efficient exploration algorithm is proposed to maximize the visited area and minimizing the risk on the generated path. In conclusion, to validate and evaluate the proposed algorithm, the mapping performance and time consumption of the proposed algorithm are compared with the Hector SLAM, ICP, and feature-to-feature registration such as corners, in static and dynamic environments. The performance of the proposed algorithm exhibits promising navigational and mapping results and very short computational time; the transformation parameters between each two successive scans are estimated in approximately 9 milliseconds, that indicates the potential use of the new proposed algorithm with real-time systems.Item Open Access Attitude Estimation Methods Using Low-cost GNSS and MEMS MARG Sensors and Their Integration(2022-09) Ding, Wei; Gao, Yang; El-Sheimy, Naser; Noureldin, AboelmagdFor low-cost magnetic, angular rate, and gravity (MARG) sensors based on the microelectromechanical system (MEMS) technology, the sensor errors and measurement noises are significantly large. Attitude errors by integrating gyro data accumulate rapidly. When the vehicle is quasi-static, the roll and pitch angles can be determined by accelerometer measurements which use the local gravity as the reference. The magnetometer is resorted to generate heading information by measuring the geomagnetic field. However, the accelerometer and magnetometer measurements can be deteriorated by the vehicle maneuver and ambient artificial magnetic disturbances, respectively. Thereby a quaternion-based error state Kalman filter (ESKF) is developed to fuse the MEMS MARG sensor measurements for accuracy improved attitude estimation. The error state vector constitutes attitude error and gyro bias variation. the gyro-measured angular rates are used to continuously propagate the vehicle’s three-dimensional attitude quaternion in its sampling rate, whilst accelerometer and magnetometer measurements are employed for the state correction. Disturbances such as external accelerations and magnetic anomalies are excluded, and the measurement noise covariance matrix is adaptively adjusted according to the innovations. Global navigation satellite system (GNSS) based attitude estimation shows time-independent error characteristics. The pitch and heading angles can be determined using a single GNSS antenna based on the time differenced carrier phase (TDCP) observations or derived from a moving baseline formed between two firmly mounted GNSS antennas. The major challenges of the former include cycle slips, carrier phase discontinuity, and slow vehicular velocity which should be excluded from attitude estimation. Whereas the integer ambiguity resolution is indispensable for the latter, the baseline length constrained least-squares ambiguity decorrelation adjustment (C-LAMBDA) method can be applied. The GNSS/MARG sensors integrated attitude estimation methods are investigated to exploit the complementary merits of the high precision of MARG sensor during the short period and the performance stability of GNSS over the long term. The ESKF developed for the MARG sensor is extended to utilize the GNSS-derived heading and pitch angles for additional measurement updates. The solution continuity is guaranteed by the MARG sensor alone during the periods when the GNSS-derived attitude angles are unavailable.Item Open Access Coherent array processing of gps sonobuoys(2010) Osman, Abdalla Mostafa; El-Sheimy, Naser; Noureldin, AboelmagdItem Embargo Continuous measurement while drilling surveying system utilizing MEMS inertial sensors(2009) Elgizawy, Mahmoud Lotfy; El-Sheimy, Naser; Noureldin, AboelmagdItem Open Access Design, Implementation and Key Issues of Adaptive Tightly Coupled MEMS INS/GPS Integration System(2017) Zhou, Qifan; El-Sheimy, Naser; Hai, Zhang; El-Sheimy, Naser; Ling, Pei; Hai, Zhang; Noureldin, Aboelmagd; Gao, Yang; Rui, ZhouTightly-coupled integrated system is advantageous in providing seamless navigation solution compared with other integration schemes, and is attractive in multiple application fields. This thesis focuses on the design, implementation of adaptive tightly-coupled integrated navigation system with the aiding of external measurement. The key issues investigated and problems solved in this dissertation are: 1. It presents four novel adaptive noise estimation approaches. These methods rely on the observation information to estimate the measurement noise property and error characteristic. The noise assessment method makes use of difference operation to eliminate the effect of other elements and this process is decoupled from the filter calculation loop. Thus, the covariance matrix estimation result will not be coupled with the state vector error, and this existed problem in traditional adaptive Kalman filter is avoided. 2. It proposes a new low-cost MEMS sensor in-filed calibration algorithm. The navigation mission requires calibration before started. The proposed approach can perform calibration of the sensors without any requirement or special needs. The calibration scheme is convenient to be accomplished by simple hand rotation in space. The bias, scale factor error and non-orthogonal error are able to be identified. 3. It investigates the Euler angle based attitude estimation. The Euler angle attitude update is constrained for further practical application owning to its singularity problem. The singularity problem will cause the attitude procedure discontinue and involves more estimation error. An intelligent coordinate switch algorithm is proposed to overcome this drawback and has achieved a good performance. The adaptive noise estimation approach is also applied in the filter to adjust the weight of observation model, which prevents the negative effect of external acceleration. 4. It introduces the adaptive noise estimation theorem and external observation in the standard tightly-coupled integrated system, and establishes the system hardware platform to test the validation. The height and heading information measured by barometer and magnetometer are used involved in measurement model to provide aiding information. A switch filter strategy is designed to save computational time, and the adaptive noise estimation approach is used to acquire the GPS measurement error characteristic. Both simulation and practical tests are conducted to verify the system.Item Open Access Developing the Use of UAV Imagery Systems for Site Specific Weed Management(2020-09-02) Hassanein, Mohamed; El-Sheimy, Naser; Lari, Zahra; Noureldin, Aboelmagd; Sousa, Mario; Wang, Cheng; Wang, RuishengThe use of Unmanned Aerial Vehicle (UAV) imagery systems for Precision Agriculture (PA) applications drew a lot of attention through the last decade. UAV as a platform for an imagery sensor is providing a major advantage as it can provide high spatial resolution images compared to satellite platform. Also, it provides the user with the ability to collect the needed images at any time along with the ability to cover the agriculture fields faster than terrestrial platform. Therefore, these UAV imagery systems are capable to fit the gap between aerial and terrestrial Remote Sensing systems. Weed management is one of the important PA applications that using UAV imagery system for it showed great potentials. The current weed management procedure depends on spraying the whole agriculture field with chemical herbicides to execute any weed plants in the field. Although such procedure seems to be effective, it has huge effect on the surrounding environment due to the excessive use of the chemical, especially that weed plants don’t cover the whole field. Usually weed plants spread through only few spots of the field. Therefore, different efforts were introduced to develop weed detection techniques using UAV imagery systems. Though the different advantages of UAV imagery systems, such systems didn’t draw the users interest due to many limitations such as the cost of these systems. The primary objective of the research work is to develop the use of UAV imagery systems for PA with focus on weed management through tackling the different limitations of using UAV imagery systems for weed management. Therefore, different methodologies are introduced for vegetation segmentation, crop row detection, and weed detection. These methodologies are able to enhance the use of low-cost UAV imagery systems through targeting two main goals. First, the use of RGB imagery sensors. Second, collect the imagery data from high altitudes.Item Open Access Enhanced UAV Navigation Under Challenging Conditions(2019-07-23) Zahran, Shady Abd El-Kader; El-Sheimy, Naser; Sesay, Abu B.; Moussa, Adel M.; Noureldin, Aboelmagd; Costa Sousa, Mario; Detchev, Ivan D.; Toth, Charles K.During the past decade, there has been an enormous increase in the applications utilizing fully autonomous Unmanned Aerial Vehicles (UAVs). Initially these applications were mostly restricted to military fields like border surveillance, troops enforcement, combat, target, and decoy. More recently they have been extended to many civilian applications, such as firefighting, traffic monitoring, and commercial UAVs. This wide variety of applications makes the UAVs autonomous navigation a challenging task. The UAVs mainly depend on the integration of Global Navigation Satellite Systems (GNSS) and Inertial Navigation System (INS) to navigate autonomously but during the lack or unavailability of the GNSS signals, the UAVs lose their ability to navigate autonomously, due to the massive drift exhibited by the INS when working in dead reckoning mode. GNSS signals may be unavailable due to jamming, spoofing, blockage, or multipath. So, another aiding system must be integrated with the INS, to yield a reliable navigation solution that makes the UAV capable of fulfilling its assigned tasks in a wider range of working conditions. Different sensors and methods were utilized to aid the navigation solution during GNSS outage. Light Detection and Ranging sensor (LiDAR) is one of the main sensors integrated with INS during these periods. The main drawback in the use of LiDAR with small and micro UAVs, is the weight, size, power consumption, and high computational cost. Camera is also considered as a viable solution to aid in navigation during the GNSS outage periods, as it is characterized by features that make it suitable for small and micro UAVs like light weight, small size, and low power consumption, also it provides rich information like features and colors. Visual information provided by a camera can be used for 3D surrounding construction, obstacle avoidance, and position estimation. One of the algorithms mostly used for exploiting camera in UAV navigation is Visual Odometry (VO), which depends on the extraction of the vehicle velocity from matching features of successive images. One of the biggest problems facing the use of a single camera is the loss of scale information. Although this issue can usually be resolved by using stereo/multiple cameras, such a solution is not so effective for small and micro UAVs, because its accuracy mainly depends on the base distance between the cameras, which is necessarily limited in such cases. Regardless of using single/multiple cameras, this sensor performance is still greatly sensitive to certain environmental factors, like light variation or featureless scenes. Radar sensor is much more superior to cameras or LiDAR, due to its immunity to environmental changes. Previously Radar’s weight, size, and power consumption were not adequate for small/micro UAVs. Ultra-Wide Band (UWB) devices have been recently considered in several UAV aided navigation works. Although their great potential to aid the UAV during loss of GNSS signals (absolute positions), due to their limited power, they can only be used in small areas (like indoors). In order to use them outdoors, a huge infrastructure is required, in addition to the availability of beacons with known positions in the flight area is not always guaranteed. So other methods or approaches that do not cost the UAV additional size, weight, cost, or power consumption is essential to achieve a robust and accurate navigation system that is able to fulfill different tasks in all circumstances with acceptable performance. First approach taken during the research is the Vehicle Dynamic Model (VDM) enhanced navigation system. The VDM is the relation between the actuators and vehicle states (accelerations and rotations). We accommodate such approach because it will not cost the UAV any additional weight, size, power, or cost. The main drawback of accommodating this approach is the requirement for a special equipment to estimate the mathematical model parameter of the UAV. Any perturbations during this modelling process will make this approach unable to aid the UAV during GNSS signals outage. In order to avoid such drawback a Machine Learning (ML) approach is adopted to take advantage of the available data from previous flights. Although this VDM- machine learning approach greatly enhanced the navigation solution compared to low-cost INS solution, but the model formed with the aid of the ML approach is specific for this UAV with this configuration, this leads to the second accommodated approach, which is enhanced UAV navigation using micro-Radar as a way to find a more robust aiding approach that’s suitable for most UAVs. Radar as a sensor is more immune to environmental changes (e.g. rain, fog, light conditions), but its weight, size, and power requirement makes it not suitable for such small UAVs. Nowadays micro Radars is available due to the advancement in technologies, which makes it a good candidate to aid small UAVs navigation system during GNSS signals outage. However, low-cost Radars come with more challenges with regards to the extraction of useful information, due to its poor performance, and the existence of large amount of clutters especially at low altitudes. Typical radar targets extractions algorithms are not suitable, especially in our case where the Radar is mounted on board of the UAV, so there is no significant difference between the targets and the background. In order to efficiently extract the targets and estimate the UAV’s velocity a computer vision-based approach is accommodated instead of the typical Radar approaches, our approach consider the output of the radar (range and velocity) as image. This approach greatly enhances the navigation solution during six minutes of complete GNSS signal outage, which reached a 2D RMSE of 5.81 m compared to INS RMSE which reached hundreds of meters. Although micro-Radar system greatly enhance the navigation solution but it’s power requirement, weight, and cost are still a burden on small UAVs. So, the Radar based navigation is followed by other approach that respect all the UAVs limitation and efficiently estimate the UAVs velocity. This final approach is based on manipulating the typical use of two sensors (Hall-effect magnetic sensor, and Mass-flow meter). Both sensors are used to efficiently estimate the velocity of a drone while respecting small drones’ limitations. By utilizing these two sensors the navigation solution is greatly enhanced during GNSS signal outage compared to low-cost INS. These two sensors specifications facilitate the ability to be merged with other sensors like camera or LiDAR to enhance the navigation solution even more.Item Open Access Handheld Mobile Mapping using Smartphones(2018-02-26) Alsubaie, Naif Muidh; El-Sheimy, Naser; Gao, Yang; Shaker, Ahmed; Noureldin, Aboelmagd; Kattan, LinaThis dissertation proposes a low-cost, handheld mobile mapping system (MMS) using smartphones. The current generation of smartphones is equipped with low-cost GPS receivers, high-resolution digital cameras, and micro-electro mechanical systems (MEMS)-based navigation sensors (e.g., accelerometers, gyroscopes, magnetic compasses, and barometers). These sensors are in fact the essential components for a MMS. However, smartphone navigation sensors suffer from the poor accuracy of global navigation satellite system (GNSS), accumulated drift, and high noise to signal ratio that are associated with inertial measurement unite (IMU). These issues affect the accuracy of the initial exterior orientation parameters (EOPs) that are input into the bundle adjustment algorithm, which then produces inaccurate 3D mapping solutions. First, the law of error propagation of variance is used to estimate the theoretical accuracy of using smartphones as handheld MMS. Then, robust sensors calibration is carried out to eliminate the deterministic errors associated with each sensor. Afterward, new methodologies are proposed to increase the accuracy of direct geo-referencing of smartphones. The prototype system was started by developing an iOS application that was to capture synchronized images with GPS and motion sensors measurements. The geo-referencing of captured mapping images was verified and improved using the proposed methodologies. This system was evaluated against ground truth data in different environments. In the absence of GPS multipath error, the RMSE of the system absolute accuracy is 3-4 meters in the horizontal direction and 13 meters in vertical direction. Furthermore, the RMSE of the system relative accuracy is 5 centimeters in the case of having more than 3 intersected light rays.Item Open Access Integration of MEMS Sensors, WiFi, and Magnetic Features for Indoor Pedestrian Navigation with Consumer Portable Devices(2016-01-21) Li, You; El-Sheimy, Naser; Niu, Xiaoji; Gao, Yang; Noureldin, Aboelmagd; Liu, Jingnan; Chen, Ruizhi; Shi, Chuang; Wu, YuanxinMobile location based services is attracting the public attention due to their potential applications in a wide range of personalized services. A demanding issue is to provide a trustworthy indoor navigation solution. This thesis provides a continous and smooth navigation solution by using off-the-shelf sensors in consumer portable devices, local magnetic features, and existing WiFi infrastructures. The main innovation points are: (a) It presents a real-time calibration method for gyro sensors in consumer portable devices. Through the use of multi-level constraints, this method happens automatically without the need for external equipment or user intervention, and reduced gyro biases from several deg/s to 0.15 deg/s indoors and 0.1 deg/s outdoors under natural human motions and in indoor environments with frequent magnetic interferences. (b) It introduces and evaluates two quality-control mechanisms for the integration of dead-reckoning (DR) and magnetic matching (MM), including a threshold-based method and an adaptive Kalman filter based method. The DR/MM results were enhanced by 47.6 % - 67.9 % and 43.9 % - 65.4 % in two environments through the use of quality control. (c) It presents a profile-based WiFi fingerprinting algorithm by using the short-term trajectories from DR and geometrical relationships of various reference points in the space. The use of the profile-based approach reduced WiFi fingerprinting errors by 14.0 %, and mitigated the WiFi mismatches when the user started navigation. (d) It proposes a WiFi-aided MM algorithm, which reduces both the mismatch rate and computational load. The WiFi-aided MM results were 70.8 % and 74.5 % more accurate than MM in two indoor environments, and 10.0 % and 10.5 % better than WiFi. (e) It designs and evaluates two improved DR/WiFi/MM integration structures and corresponding quality-control mechanisms. Structure #1 utilizes the WiFi-aided MM algorithm, while Structure #2 uses the integrated DR/WiFi solutions to limit the MM search space. This mechanism in Structure #2 has at least one more level than those in previous DR/WiFi/MM structures. The difference between the Structure #2 results in two indoor environments were 13 %, and the difference between the Structure #2 results under four different motion conditions were 16 %.Item Open Access Map aided Low cost MEMS Based Pedestrian Navigation Applications(2018-08-07) Yu, Chunyang; El-Sheimy, Naser; Gao, Yang; Noureldin, Aboelmagd; Gao, Wei; Li, YibingNowadays, indoor pedestrian location system has a big market requirement, more than 25000 developers in the world are focusing on this market. Various kinds of techniques, such as map based method, inertial navigation based method, Wi-Fi based positioning, Bluetooth technique, vison based technique, could be used to obtain the pedestrian’s position in indoor environment. However, each method has its own drawbacks, therefore, numerous methods have been proposed and integrated for pedestrian navigation by researchers. To date, the major challenges for an indoor pedestrian navigation system is to reduce the cost of the system, including the time-cost and the economic-cost, without decreasing the accuracy of the system. Considering that the MEMS sensor-based inertial sensors are low-cost, convenient, and self-independent, and the global IMU embedded smartphone adoption rate keeping increasing year by year. Therefore, inertial navigation based method is applied in this research to obtain a primary navigation solutions. However, the estimated solution of INS grows with time. Moreover, the accuracy of most smartphone embedded MEMS sensors is not as good as traditional inertial sensors. Specifically, MEMS gyro errors can cause heading errors and position errors; MEMS accelerometer error affect steps detection of Zero Velocity updates. Therefore, aiding constrains, such as Non-Holonomic constraints and Zero Velocity updates are used to correct the inertial navigation errors. In smart cities, the coverage rate of Wi-Fi keeps increasing, and the widespread distribution of Wi-Fi makes Wi-Fi suitable for indoor positioning. Take advantage of the pre-existing Wi-Fi access points, the Wi-Fi fingerprinting estimated positioning can be used to integrate with inertial navigation solutions. However, Wi-Fi signal is not accessible anytime and anywhere. So, Wi-Fi position is only an alternative aiding information for the proposed indoor position system. Map-based navigation is a traditional way to locate a pedestrian, and it is a low-cost method, which does not need any aiding infrastructures. Currently, most of the public building can provide indoor digital maps to users. Therefore, indoor map information can be added to inertial navigation system to improve the inertial navigation solutions. Map Matching and Map Aiding algorithm are novelty integrated in this research to effectively use the free map information. Map Aiding is accommodating and does not need any assumptions about the path of the user. Map Matching is used for fixed trajectory part, such as corridors in buildings. Two methods are used in this research to complement each other, Map Matching will be added on the map-aided INS solution. A cascade connected Extended Kalman filter and Auxiliary particle filter integration algorithm which comprised a double-deck architecture is presented in this research to fuse all the above information. This structure can take advantage of merits of Extended Kalman filter and Auxiliary particle filter to estimate the navigation solution. The underlying Extended Kalman filter uses Zero Velocity and Non-Holonomic constraints as inputs of Extended Kalman filter to improve the preliminary INS navigation results. To verify the proposed methods, experiments in different scenarios are conducted in different scenarios. The test results clearly indicate that the cascade structure algorithm can reduce the computational burden of the system. Also, through the proposed methodologies, integrating indoor map information, smartphone embedded sensors, and the pre-existing Wi-Fi, the indoor position system could provide continuous, accurate, and low-cost positions for pedestrians in indoor environments.Item Open Access MEMS-based Aided Inertial Navigation System for Small Diameter Pipelines(2016) Sahli, Hussein; El-Sheimy, Naser; Tarbouchi, Mohammed; Gao, Yang; Noureldin, Aboelmagd; El-Badry, MamdouhPipeline Inspection Gauges (pigs) have been used for many years to perform various maintenance operations in oil and gas pipelines. Different pipeline parameters can be inspected during the pig journey. Although, pigs uses many sensors to detect the required pipeline parameters, matching these data with the corresponding pipeline location is considered a very important parameter that needs to be estimated. High-end, tactical-grade Inertial Measurement Units (IMUs) are used in pigging applications to locate the detected problems of pipeline using other sensors, and to reconstruct the trajectories of the pig. These IMUs are accurate; however, their high cost and large sizes limit their use in small diameter pipelines. Calibration would improve the accuracy of the uncertainties that exist in sensor errors behavior. However, intensive calibration would also increase the cost of using IMUs. Therefore, another way to improve the accuracy is used by augmenting IMU with aided sensors (i.e. odometers). This thesis describes a new methodology for the use of low-cost IMUs using an extended Kalman filter (EKF) and the pipeline junctions to increase the navigation parameters’ accuracy and to reduce the total RMS errors even during the unavailability of Above Ground Markers (AGMs). The results of this new proposed method using micro-electro-mechanical systems (MEMS) based IMU revealed that the position RMS errors were reduced by approximately 85% of the standard EKF solution. Therefore, this approach will enable the mapping of small diameter pipelines, which was not possible before.Item Open Access Multimodal Spatiotemporal Collaborative Positioning Framework for Indoor Environments(2019-07-10) Sakr, Mostafa; El-Sheimy, Naser; Gao, Yang; Noureldin, Aboelmagd; O'Keefe, Kyle P. G.; Hassanein, Hossam S.This thesis proposes and evaluates a unified collaborative and multimodal framework for indoor positioning and mapping using smartphones. The proposed framework aims to harness the potential of collaboration between different nodes for the positioning and mapping tasks, using only smartphones, without assuming the existence of any specific infrastructure. This objective is achieved by first exploring and enhancing the different building blocks of the proposed framework; followed by evaluating the accuracy gains from using a collaborative approach to the positioning problem. The first building block to be studied is the standalone navigation filter. The standard extended Kalman filter, the unscented Kalman filter, and the particle filter were evaluated for node positioning using the pedestrian dead reckoning model as a system model, while the measurement update is achieved using Wi-Fi fingerprinting with a Gaussian process model. The second component of the system is the Wi-Fi radio map. The proposed framework utilizes a new sparse Gaussian process model to represents the Wi-Fi radio map, used for Wi-Fi signal strength-based fingerprinting. The map building algorithm using the proposed model and its performance are presented and discussed. The collaboration between different nodes is examined in detail, and a new family of distributed particle filters for collaborative positioning applications are introduced. The detailed derivation of the filtering equation along with simulation evaluation of the filters are presented. The collaboration model used in the proposed framework is based on the relative range measurements. A ranging device based on ultra-wideband (UWB) technology is designed and implemented to evaluate the framework. The ranging device is based on the DW1000 UWB transceiver from Decawave. The device can reach centimetre-level ranging accuracy and connects to a host microcontroller which controls the flow of ranging messages, computes the range, and communicate with a paired smartphone through Bluetooth Low Energy interface. On the smartphone, a logging application saves the range information from the UWB device along with other sensors data such as accelerometer, gyroscope, magnetometer, pressure, and Wi-Fi signal strength. Along with this software, a simulation environment is developed to model the motion of random nodes inside an indoor environment. This simulator was used in the evaluation of the proposed particle filters family. The thesis concludes by evaluating the proposed framework using multiple test trajectories and different operating scenarios in a challenging indoor environment.Item Open Access Multiple Systems Integration for Pedestrian Indoor Navigation(2016) Lan, Haiyu; El-Sheimy, Naser; Zhao, Yuxin; Gao, Yang; El-Sheimy, Naser; Zhao, Yuxin; Noureldin, Aboelmagd; Yin, Shen; Zhang, YonggangNumerous solutions to solve existing problems of pedestrian navigation have been proposed in the last decade by both industrial and academic researchers. However, to date, there are still major challenges for a pedestrian navigation system (PNS) to operate continuously, robustly, and seamlessly in all indoor/outdoor environments. Since the Global Navigation Satellite System (GNSS) is reliable under most outdoor environments, novel methods for pedestrian indoor navigation applications through integrating different navigation techniques and systems were proposed in this thesis. A PNS architecture based on a single device was first introduced, integrating the inertial navigation system (INS) mechanization, the pedestrian dead reckoning (PDR) mechanization, and the WiFi fingerprinting positioning method. Experimental evaluation showed that the proposed WiFi/PDR/INS algorithm not only tracks closest to the actual pedestrian walking trajectories but also provides the navigation results with good continuity. When multiple PNSs are used simultaneously by a specific user, novel information fusion methods for multiple PNSs integration to enhance the performance of each PNS are proposed. A nonlinear inequality distance constraint between any two PNSs was mathematically formulated. A novel filtering technique named the state-constrained Kalman filter (KF) was used to explore such a constraint information, further diminishing the positioning errors of each PNS. Two different approaches based on the state-constrained KF for solving the multiple PNSs integration problem were proposed. The first approach incorporates a soft constraint into a KF to enable the state estimate almost satisfies the constraint rather than strictly satisfies the constraint; the second approach is based on solving a Quadratic Programming (QP) problem to ensure that the state estimate should strictly satisfy the constraint. Simulation studies and field experiments were conducted to assess the two proposed approaches. The results showed that both approaches could well bound the navigation state errors compared with the unconstrained state estimate. However, the performance of the hard constraint approach was better than that of the soft constraint approach when a constraint’s nonlinearity level increased. It is indicated from this research that using motion sensor data from multiple mobile devices could provide more accurate navigation solutions for a pedestrian in all indoor/outdoor environments.Item Open Access Navigation of UAV in Denied GNSS Environments Using Multi-Sensor Systems(2018-08-10) Mostafa, Mostafa Mohamed Ahmed; El-Sheimy, Naser; Sesay, A. B.; Noureldin, Aboelmagd; Sharlin, Ehud; Shabazi, Moshdeh; El-Tokhey, Mohamed E.There have been extensive market demands over the past 10 years for deploying small autonomous Unmanned Aerial Vehicles (UAVs) in enormous civil and military applications such as search and rescue, disaster management, firefighting, reconnaissance and border mentoring. While UAVs are performing their missions, they are typically relying on the onboard Global Navigation Satellite System (GNSS)/ Inertial Navigation System (INS) integrated navigation system for the positioning and localization purpose. During such missions, the GNSS signals could be prone to blockage, attenuation, multipath effect, jamming and spoofing. In such complicated scenarios, the navigation solution is acquired by the INS in standalone mode prior to the GNSS signals recovery. Consequently, the navigation solution will deteriorate rapidly because of the drift exhibited by the low-cost INS during GNSS signal outages. Therefore, the necessity for an accurate and reliable navigation system in such cluttered environments is essential to achieve their missions. A variety of sensors and techniques have been exploited in an attempt to provide a reliable navigation solution in GNSS-denied environments. Although these sensors have some strengths individually, they still suffer from some limitations. Monocular Visual Odometry (VO) has been proposed as a GNSS denied environment navigation system for UAVs since it has light weight, small size and low power consumption. This monocular VO suffer from the scale ambiguity if there is no other aiding sensor or prior information of the observed scene. Furthermore, it depends on a rigorous calibrated camera and system model which may change from one flight to another or even during the flight. Therefore, a novel monocular VO based on optical flow and regression tress is proposed which eliminates the need for a calibration phase and inherently models the interior camera parameters, its lever arm and boresight parameters since, the relationship between the actual optical flow vectors and the navigation states are implicitly modeled during the flight. In addition, this monocular VO can resolve the scale ambiguity problem by implicitly modeling the scale on its trained regression model. Although this monocular VO has such capabilities and benefits, its 3D positioning accuracy is still affected by some factors such as the lack of the observed features, inconsistent matches, and the accumulated positioning drift errors. Hence, a smart hybrid vision aided inertial navigation system (VAINS) is proposed to correct both monocular VO and INS drift errors based on trained Gaussian Process Regression (GPR) against GNSS reference data. Although a variety of VO based approaches have been proposed to enhance the navigation solution during the GNSS signal outage, their imagery measurements are affected by brightness, lighting conditions and featureless areas. In addition, their measurements are not immune against the environmental conditions such as rain, fog and dust which could affect their usage as a GNSS denied environment navigation system. In order to avoid such limitations, a lightweight Frequency Modulated Continuous Wave (FMCW) Radar Odometry (RO) aided navigation system is proposed as a GNSS denied environment navigation system for UAVs. This system is immune to these environmental changes and it has light weight, small size, and low power consumption which make it more appealing to be mounted on small UAVs. Although the camera has some strengths and limitations, its incorporation with radar will enhance the performance and will provide a more reliable navigation solution. In addition, the scale ambiguity of the monocular VO is resolved by the estimated RO height. Furthermore, this integrated system is more robust against the environmental conditions since the radar is immune against these environmental changes.Item Open Access Navigation Sensor Stochastic Error Modeling and Nonlinear Estimation for Low-Cost Land Vehicle Navigation(2023-09-12) Minaretzis, Chrysostomos; El-Sheimy, Naser; Noureldin, Aboelmagd; Gao, Yang; Yang, Hongzhou; Hefnawi, MostafaThe increasing use of low-cost inertial sensors in various mass-market applications necessitates their accurate stochastic modeling. Such task faces challenges due to outliers in the sensor measurements caused by internal and/or external factors. To optimize the navigation performance, robust estimation techniques are required to reduce the influence of outliers to the stochastic modeling process. The Generalized Method of Wavelet Moments (GMWM) and its Multi-signal extensions (MS-GMWM) represent the latest trend in the field of inertial sensor error stochastic analysis, they are capable of efficiently modeling the highly complex random errors displayed by low-cost and consumer-grade inertial sensors and provide very advantageous guarantees for the statistical properties of their estimation products. On the other hand, even though a robust version exists (RGMWM) for the single-signal method in order to protect the estimation process from the influence of outliers, their detection remains a challenging task, while such attribute has not yet been bestowed in the multi-signal approach. Moreover, the current implementation of the GMWM algorithm can be computationally intensive and does not provide the simplest (composite) model. In this work, a simplified implementation of the GMWM-based algorithm is presented along with techniques to reduce the complexity of the derived stochastic model under certain conditions. Also, it is shown via simulations that using the RGMWM every time, without the need for contamination existence confirmation, is a worthwhile trade-off between reducing the outlier effects and decreasing the estimator efficiency. Generally, stochastic modeling techniques, including the GMWM, make use of individual static signals for inference. However, it has been observed that when multiple static signal replicates are collected under the same conditions, they maintain the same model structure but exhibit variations in parameter values, a fact that called for the MS-GMWM. Here, a robust multi-signal method is introduced, based on the established GMWM framework and the Average Wavelet Variance (AWV) estimator, which encompasses two robustness levels: one for protection against outliers in each considered replicate and one to safeguard the estimation against the collection of signal replicates with significantly different behaviour than the majority. From that, two estimators are formulated, the Singly Robust AWV (SR-AWV) and the Doubly Robust (DR-AWV) and their model parameter estimation efficiency is confirmed under different data contamination scenarios in simulation and case studies. Furthermore, a hybrid case study is conducted that establishes a connection between model parameter estimation quality and implied navigation performance in those data contamination settings. Finally, the performance of the new technique is compared to the conventional Allan Variance in a land vehicle navigation experiment, where the inertial information is fused with an auxiliary source and vehicle movement constraints using the Extended and Unscented Kalman Filters (EKF/UKF). Notably, the results indicate that under linear-static conditions, the UKF with the new method provides a 16.8-17.3% improvement in 3D orientation compared to the conventional setting (AV with EKF), while the EKF gives a 7.5-9.7% improvement. Also, in dynamic conditions (i.e., turns), the UKF demonstrates an 14.7-17.8% improvement in horizontal positioning and an 11.9-12.5% in terms of 3D orientation, while the EKF has an 8.3-12.8% and an 11.4-11.7% improvement respectively. Overall, the UKF appears to perform better but has a significantly higher computational load compared to the EKF. Hence, the EKF appears to be a more realistic option for real-time applications such as autonomous vehicle navigation.Item Open Access New measurement-while-drilling surveying technique utilizing sets of fibre optic rotation sensors(2002) Noureldin, Aboelmagd; Mintchev, Martin P.Item Open Access Non-linear Error Modeling for MEMS-based IMUs(2018-12-14) Radi, Ahmed; El-Sheimy, Naser; Sesay, Abu B.; Nassar, Sameh; Noureldin, Aboelmagd; Ghannouchi, Fadhel M.; Hamad, Ahmed M.The precise estimation of the position, velocity and orientation of a moving object with and without reception of satellite signals using low-cost sensors has always been a challenging task. Current navigation market is dominated by integrating satellite positioning, such as Global Navigation Satellite System (GNSS) with Inertial Navigation Systems (INSs) through Bayesian filters; e.g. Kalman Filter (KF). During satellite positioning signal outages, navigation information is provided using the inertial sensors, i.e. the gyroscopes and accelerometers of an Inertial Measurement Unit (IMU). Thus, the overall quality of integrated navigation systems is driven by inertial sensors errors. This thesis aims at improving inertial sensor stochastic error modeling to obtain better accuracy, especially in INS stand-alone mode. A common approach to model inertial sensor stochastic errors (sometimes known as stochastic noise) is a 1st order Gauss-Markov (GM) process where its parameters are estimated using the Autocorrelation sequence of the sensor static measurements output collected at room temperature. However, the stand-alone 1st order GM model has proven not to be the best model for several inertial sensors. Consequently. in this thesis different and better noise characterization approaches are proposed, developed and used for analyzing such inertial sensor stochastic noise. The stochastic characteristics of low-cost Micro-Electro Mechanical Systems (MEMS)-based inertial sensor errors and their changes according to temperature and platform dynamics variation using two different approaches, namely Allan Variance (AV) and Generalized Method of Wavelet Moments (GMWM), are investigated. Advantages and limitations of each method concerning the ability to 1) identify the latent random processes associated with the detected error model and 2) accurately estimate the parameters of each random process; are highlighted and used to provide justifications for the developments brought afterword. A new wavelet variance-based framework, as an extension to the standard GMWM, for multi-signal inertial sensor calibration is proposed and developed in this thesis, namely Multi-Signal GMWM (MS-GMWM) that allows to model complex composite stochastic processes. The proposed approach not only can improve the modeling of stochastic sensor errors by using multiple replicates from a calibration procedure but also allows to understand the properties of these stochastic errors to perform more efficient calibration and, consequently, improve the navigation performance. In addition, a Graphical User Interface (GUI) algorithm is developed to make the MS-GMWM available to the general user and to facilitate the calibration procedures of inertial sensor errors using several complex stochastic error models. The KF design accounting for inertial sensor complex stochastic error models is investigated including detailed mathematical explanation of both the prediction and update stages. A novel environmentally-dependent (i.e. taking into account dynamics and temperature changes) adaptive integrated navigation algorithm is developed in this thesis, which is adapted to switch between different stochastic error parameters values according to 1) the inertial sensor temperature and 2) the platform dynamics to limit the overall environmental-dependent effects. The performance of the constructed stochastic error models, when operated through the proposed adaptive integrated algorithm in the designed GUI platform filter presented with optional adaptivity features, is evaluated using field real INS/GNSS data with induced GNSS signal outages. Compared to the traditional 1st order GM model, results showed that considering more complex error models, based on dynamics and thermal data analysis, improves the positioning errors during GNSS signal outages by 32.36 - 51.19%, which shows the significant effect of the proposed algorithms in this thesis.Item Open Access S-PDR: A Novel Pedestrian Dead Reckoning Algorithm with step-based attitude corrections for Free-Moving Handheld devices(2021-01-08) khedr, maan E.; El-Sheimy, Naser; Noureldin, Aboelmagd; Gao, Yang; O'Keefe, Kyle; Fapojuwo, Abraham O.; Chen, RuizhiMobile location-based services (MLBS) are attracting attention for their potential public applications and personal use. MLBS can be used for a variety of applications such as location-based advertisement, smart shopping, smart cities, health applications, emergency response, and even gamming. The majority of these applications are used in indoor environments where the well established GNSS navigation solutions are hindered or even unavailable and hence they rely on alternative navigation solutions such Inertial Navigation Systems (INS). To date, the main challenges for MLBS is to provide accurate and reliable navigation solution under varying circumstances such as indoor or outdoor, while reducing system cost and having real-time applicability, which is achieved through the use of MEMS technology. However, MEMS sensors suffer from high errors and noise to signal ratio that results in quick divergence of the INS solution, hence the need for aiding. This thesis aims at providing a Pedestrian Dead Reckoning (PDR) solution that uses off-the-shelf sensors in mobile devices to provide short term reliable navigation solution that helps reduce the complexity and frequency of relying on aiding techniques through developing a novel PDR system S-PDR . S-PDR utilizes a novel step detection technique that is motion-mode and use-case invariant, an attitude correction technique that can provide corrections as frequently as a step-by-step basis, and an enhanced PCA-based heading estimation. Testing results in comparison to XSense MTi G-710 which is a high-end MEMS sensor show that S-PDR provide reliable short-term navigation solution with final positioning error that is up to 6 meters after 3 minutes operation time, outperforming the on-board fusion solution provided by the XSense. The short term enhancement of the PDR solution reliability 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.Item Open Access Vision Sensor Aided Navigation for Ground Vehicle Applications(2019-01-11) Liu, Zhenbo; El-Sheimy, Naser; Qin, Yongyuan; Gao, Yang; Noureldin, Aboelmagd; Li, Sihai; Wang, YuegangManned or unmanned ground vehicles with autonomous ability have attracted people’s attention greatly in recent decades. As a result, there is an increased demand for the navigation performance improvement of low-cost navigation systems. The integration of INS and GNSS receivers is well-known and commonly used in ground vehicle applications, not only because the two sensors have complementary characteristics, but also their integration can provide position and orientation in a global scale. However, GNSS signals can suffer from obstruction and multi-path errors in city canyons, tunnels, woodlands, and mountainous regions. They are also vulnerable to jamming and spoofing. Therefore, the navigation in GNSS-denied environment is of interest among a lot of researchers. It is significant to study the methods to mitigate the error drift by using low-cost navigation sensors and aiding sensors, as well as the new integration schemes and techniques, especially using knowledge from multiple disciplines. The content of this thesis is as follows. 1. In the non-holonomic constraints (NHC) and odometer (OD) aided navigation system, the system model fully considers the inter-sensor calibration parameters, such as the boresight error and lever-arm of IMU with respect to the vehicle frame. Considering the characteristic of low-cost IMU sensors, the observability of INS/NHC/OD integration is theoretically analyzed, which is different from the existing high-end INS case. To deal with large boresight errors and to obtain higher inter-sensor calibration accuracy, we propose to use Unscented Kalman filter (UKF) as the fusion scheme, taking special treatment on the unscented transform to the quaternion. Simulation test shows that UKF outperforms EKF in estimating the calibration parameters, especially when the boresight error is slightly larger. A new attitude-velocity constraint aided INS is developed, which has the theoretical equivalence with NHC. The vehicle experiments demonstrate that with the help of this constraint, the positioning RMS error is within 0.7m during 60s GNSS outages for the IMU with $1^\circ/h$ gyro bias. 2. We propose the vanishing point-aided INS method based on the parallel lane marking observations from a forward-looking camera. There are two cases: one is when the lane orientation is unknown and the other is known lane orientation with the help of digital maps. We develop the mathematical relationship between the vanishing point coordinates and relative attitude of the camera with respect to the road. Based on this, the relative heading formula is derived. The whole VP aiding scheme is proposed, including the straight lane detection, uncertainty analysis, sequential Kalman filtering, and sensitivity analysis of INS/VP integration. The AIME (Autonomous Integrity Monitored Extrapolation) soft failure scheme is adopted to detect the small curve of the lane. The algorithm is tested by the simulations and experiments. It is shown that with additional help of VP, 33\% improvement of the positioning accuracy is achieved than INS/NHC alone, reaching 0.32\% DT (distance travelled). 3. We propose to use the relative pose from a monocular camera to aid the INS. The frame to frame relative pose is calculated based on the epipolar constraint. An uncertainty estimation method for the relative attitude from the vision system is developed, which is essential for the sensor fusion. Simulations and experiments show the validness of the covariance estimation method. A simple but effective failure detection method of the VO system is proposed based on the translation vector from VO. Finally, the loosely-coupled INS/NHC/VO integration is developed, and the observability analysis proves the complementary properties of INS/NHC and INS/VO integration. The experiments show that in the INS/NHC/VO integrated navigation, the average horizontal positioning RMS error of 4 experiments is within 0.30\% DT. 4. The line features observed by a camera are extracted and parameterized for further improving the accuracy of existing VINS. The first approach is developed to extract the lines corresponding to the vertical 3D lines of buildings and thus to calculate the roll angle of the vehicle. This helps the existing point feature based VINS using Multiple State Constraint Kalman filter (MSCKF). Furthermore, a new straight line parameterization, which is called Anchored Inverse-Depth Pl\"{u}cker Line (AIDPL), is proposed for the undelayed initialization of 3D space lines when using line-based VINS under the framework of EKF-SLAM. The Monte Carlo simulation tests demonstrate that the positioning accuracy is significantly improved using proposed tightly-coupled VINS. Meanwhile, the 3D lines in the environment are estimated effectively and quickly in the setup.