Browsing by Author "El-Sheimy, Naser"
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- ItemOpen Access3D Indoor Mobile Mapping using Multi-Sensor Autonomous Robot(2015-10-01) Mostofi, Navid; El-Sheimy, NaserAutonomous indoor mobile mapping has opened up new horizon in the field of surveying and mapping industry. The ability to create a 3D map without user intervention not only reduces labour costs but also provides more flexibility for exploring remote sites. Hence, it is worthwhile to consider the role of robotics in the mapping industry. The primary demand for autonomous robot systems is to interact with environment for obstacle avoidance and self-localization in six degrees of freedom (x-, y-, z-position, roll, yaw and pitch angle). The later issue requires knowledge of the operating environment, which leads to automatic environment modeling or environment mapping solution. Two different scenarios for autonomous indoor mobile mapping are investigated in this thesis. The first scenario is based on the use of a single RGB-D sensor to map a small room of size (8x8 meter). In the second scenario RGB-D sensor is used as an aiding sensor for Velodyne HDL-32 LiDAR to map a large corridor of size (33x11 meter). The results shows that the solution of single RGB-D sensor is accurate enough for mapping a small room; however, for large corridor the result of RGB-D aided Velodyne HDL-32 generated more accurate and consistent mapping solution. The main challenge that should be handled for autonomous mapping is alignment of multiple local scans as they become locally distorted because of the motion of the platform and noise in sensor measurements. The collected scans from multiple locations are associated with the individual sensor locations (the capturing process is done using stop-and-go approach, where the robot is stopped at different locations to capture the scene). Hence, a registration process must be performed in order to combine several scans at different locations. The main goal of the registration process is to estimate the transformation parameters, which will define the relation between the collected datasets from different locations. The optimization and enhancement of the registration procedure plays a major role for generating indoor mobile mapping solution. The problem of alignment is addressed through several optimization steps, starting from coarse registration, followed by fine registration, segmentation and finally loops closure.
- ItemOpen AccessA cross-layer design for wireless location(2008) Youssef, Mohamed; El-Sheimy, Naser; Fattouche, Michael
- ItemOpen AccessA feasibility study for real-time detection and georeferencing of forest fire hot spots(2004) Wright, David Bruce; El-Sheimy, Naser
- ItemOpen AccessA framework for multi-platform mobile mapping data registration(2009) Hassan, Taher Fathy; El-Sheimy, Naser
- ItemOpen AccessA 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.
- ItemOpen AccessA Mobile MEMS-based Sensors System for Ubiquitous Personal Navigation(2015-08-24) Zhao, Xing; El-Sheimy, NaserGlobal Navigation Satellite Systems (GNSS) are widely used for most navigation applications. However, GNSS quality and availability suffer greatly in certain environments, such as urban canyons, or indoors due to signal blockage. This thesis investigates estimation algorithms to integrate data from multiple low-cost MEMS sensors in a personal navigation system to bridge those signal gaps. MEMS-based accelerometer, gyroscope, magnetometer, and barometer sensor technologies are surveyed in depth. The main sensor design parameters and their connection to navigation performance are presented. Furthermore, this thesis presents a way of decomposing the sensor error terms then applying proper stochastic and deterministic error models. Subsequently, navigation estimation states and online calibration methods are elaborated. Several key sensors-based positioning algorithms are explored. First, a nine-axis fusion engine of accelerometers, gyroscopes, and magnetometers is formulated into an attitude Kalman filter for orientation determination. Then a Pedestrian Dead Reckoning (PDR) algorithm is developed based on the accelerometer’s step detection and stride length estimation with the heading determined from the attitude fusion filter. In addition, Wi-Fi positioning is investigated for indoor environments based on received signal strengths. Finally altitude integration of the barometer and GPS height measurements is introduced to improve vertical position accuracy. The complete navigation system is constructed using an Extended Kalman Filter (EKF) to perform the data fusion from multiple positioning above. This thesis also introduced the observability analysis for quantitative analysis about the degree of observability of each estimated state in EKF. Field tests are presented to verify the system and developed algorithms using three different portable navigation prototypes. The first prototype explores optimal integration of the PDR and GPS for a continuous positioning solution. The second prototype is focused on Wi-Fi assistance when GPS is not available in deep indoor environments. The third prototype is a more compact form factor design that mimics the smartphone experience in real-life applications. The results show that the prototype systems can effectively deal with short GPS signal outages using EKF. Thus this thesis shows a cost effective design for a mobile, reliable and accurate system that enables continuous navigation anywhere.
- ItemOpen AccessA new approach for simplification of linear vector data for internet_based GIS applications(2002) Shahriari Namini, Nadia; El-Sheimy, Naser
- ItemOpen AccessAccuarcy improvement of low cost INS/GPS for land applications(2001) Shin, Eun-Hwan; El-Sheimy, Naser
- ItemOpen AccessAccuracy enhancement of integrated MEMS-IMU/GPS systems for land vehicular navigation applications(2005) Abdel-Hamid, Walid; El-Sheimy, Naser
- ItemOpen AccessAdjustment of satellite-based ranging observations for precise positioning and deformation monitoring(2002) Radovanovic, Robert Slobodan; Teskey, William F.; El-Sheimy, Naser
- ItemOpen AccessAn analysis on the optimal combination of geoid, orthometric and ellipsoidal height data(2003) Fotopoulos, Georgia; Sideris, Michael G.; El-Sheimy, Naser
- ItemOpen AccessAn intelligently tuned orientation estimation for pedestrian dead reckoning using handheld devices(2012) Siddharth, Siddharth; El-Sheimy, Naser
- ItemOpen AccessAnalysis of integrated sensor orientation in aerial mapping(2005) Ip, Alan Wing Lun; El-Sheimy, Naser
- ItemOpen AccessAssessment and attenuation of movement disorder motion using inertial sensors(2011) Teskey, Wesley; El-Sheimy, Naser; Lichti, Derek
- ItemOpen AccessAttitude 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.
- ItemOpen AccessAutomated urban features classification and recognition from combined rgb/lidar data(2011) Eid, Hassan Elsaid Elhifnawy; El-Sheimy, Naser
- ItemOpen AccessBehavior-based and Contextual Location Recommendation for Location Based Social Networks(2021-02-10) Rahimi, Seyyed Mohammadreza; Wang, Xin; Far, Behrouz Homayoun; El-Sheimy, Naser; Liang, Steve H. L.; Uddin, Gias; Wachowicz, MónicaLocation-Based Social Networks (LBSNs) are social networks with functionalities that let users share their location information with other users. A service capable of improving user engagement and bring more check-ins to the Location-based Social Network is location recommendation. Location recommendation is the task of suggesting unvisited locations to the users. An effective location recommendation model makes user-specific recommendations based on users’ preferences, geographical constraints and contextual information such as time and weather. The main question we need to answer to design a location recommendation model is how to effectively utilize different types of information into location recommendation. In this thesis, Behavior-based Location Recommendation (BLR) and Contextual Location Recommendation (CLR) are proposed, these models effectively utilize temporal and other contextual information to produce improved location recommendations. In this thesis we investigate the development of two novel location recommendation methods for Location-Based Social Networks (LBSNs), the Behavior-based Location Recommendation (BLR) and the Contextual Location Recommender (CLR). The BLR finds the temporal and spatial patterns of users’ behaviors in the form of temporal and spatial probability distributions. It then uses the patterns to predict the location type and recommends nearby locations of that type to the user. On the other hand, the CLR method first extracts the responses of the users to contextual triggers using their check-in history. It then utilizes a tensor factorization technique to extract common responses and predict the user response with the given set of contextual triggers. Finally, CLR filters locations in the user’s proximity based on the predicted location type. To find user similarities, both BLR and CLR utilize Random Walk with Restart. To improve the performance of these methods, an optimized random walk with restart method is also proposed that can improve the time complexity of random walk with restart by a factor of at least 6.75. Both BLR and CLR methods go through a set of experiments on a real-world check-in dataset. These experiments show that BLR and CLR methods improve the performance of the existing location recommendation methods in terms of precision and recall. Additionally, both BLR and CLR methods can achieve higher precision and recall values for cold-start users compared to the well-known baseline models.
- ItemOpen AccessCalibration of airborne laser scanners(2002) Morin, Kristian Walker; El-Sheimy, Naser
- ItemOpen AccessCoherent array processing of gps sonobuoys(2010) Osman, Abdalla Mostafa; El-Sheimy, Naser; Noureldin, Aboelmagd
- ItemOpen AccessComputer Vision Aiding Smartphone Sensors for Indoor Location Applications(2015-01-23) Kazemipur, Bashir; El-Sheimy, NaserModern mobile phones are powerful processing devices with a host of onboard technologies of interest to navigation system designers. In the absence of Global Navigation Satellite System (GNSS) information, the accelerometers and gyroscopes within a smartphone can be used to provide a relative navigation solution. However, these micro-electro-mechanical systems (MEMS) based sensors suffer from the effects of various errors which cause the inertial-only solution to deteriorate rapidly. As such, there is a need to constrain the inertial positioning solution when long-term navigation is needed. GNSS positions and velocities, and WiFi positions when available, are the most important forms of updates available for the inertial solution. However, updates from these two sources depend on external signals and infrastructure that may not always be available. One attractive source of updates is through the use of a vision sensor. This work describes the development of a vision-based module that determines the device heading misalignment and context based on a sequence of images captured from the device camera. The vision aiding module checks for static periods and calculates the device heading misalignment when in motion. Context classification is assessed for five common use cases: (1) fidgeting the phone while standing still (“fidgeting” context), (2) phone on ear on one floor (“single floor calling” context), (3) phone on ear on stairs (“stairs calling” context), (4) phone in hand on a single floor (“single floor texting” context), and (5) phone in hand on stairs (“stairs texting” context). The module was tested using real-time video and inertial data collected using a Samsung Galaxy S3 smartphone running the Android 4.0 operating system. The results show successful detection of the aforementioned use cases and accurate device angles. Integration of the vision aiding module with a pedestrian dead reckoning (PDR) system shows improvements to the position solution.