Journal articles: 'GPS-Denied navigation'

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Suzuki, Satoshi. "Integrated Navigation for Autonomous Drone in GPS and GPS-Denied Environments." Journal of Robotics and Mechatronics 30, no. 3 (June 20, 2018): 373–79. http://dx.doi.org/10.20965/jrm.2018.p0373.

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In this study, a novel robust navigation system for a drone in global positioning system (GPS) and GPS-denied environments is proposed. In general, the drone uses position and velocity information from GPS for guidance and control. However, GPS cannot be used in several environments; for example, GPS exhibits huge errors near buildings and trees, indoor environments. In such GPS-denied environments, a Laser Imaging Detection and Ranging (LIDAR) sensor-based navigation system has generally been used. However, the LIDAR sensor also has a weakness, and it cannot be used in an open outdoor environment where GPS can be used. Therefore, it is advantageous to develop an integrated navigation system that operates seamlessly in both GPS and GPS-denied environments. In this study, an integrated navigation system for the drone using GPS and LIDAR was developed. The design of the navigation system is based on the extended Kalman filter, and the effectiveness of the developed system is verified by numerical simulation and experiment.

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Lee, Jong Ki, Dorota A. Grejner-Brzezinska, and Charles Toth. "Network-based Collaborative Navigation in GPS-Denied Environment." Journal of Navigation 65, no. 3 (March 23, 2012): 445–57. http://dx.doi.org/10.1017/s0373463312000069.

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Global Positioning System (GPS) has been used as a primary source of navigation in land and airborne applications. However, challenging environments cause GPS signal blockage or degradation, and prevent reliable and seamless positioning and navigation using GPS only. Therefore, multi-sensor based navigation systems have been developed to overcome the limitations of GPS by adding some forms of augmentation. The next step towards assured robust navigation is to combine information from multiple ground-users, to further improve the chance of obtaining reliable navigation and positioning information. Collaborative (or cooperative) navigation can improve the individual navigation solution in terms of both accuracy and coverage, and may reduce the system's design cost, as equipping all users with high performance multi-sensor positioning systems is not cost effective. Generally, ‘Collaborative Navigation’ uses inter-nodal range measurements between platforms (users) to strengthen the navigation solution. In the collaborative navigation approach, the inter-nodal distance vectors from the known or more accurate positions to the unknown locations can be established. Therefore, the collaborative navigation technique has the advantage in that errors at the user's position can be compensated by other known (or more accurate) positions of other platforms, and may result in the improvement of the navigation solutions for the entire group of users. In this paper, three statistical network-based collaborative navigation algorithms, the Restricted Least-Squares Solution (RLESS), the Stochastic Constrained Least-Squares Solution (SCLESS) and the Best Linear Minimum Partial Bias Estimation (BLIMPBE) are proposed and compared to the Kalman filter. The proposed statistical collaborative navigation algorithms for network solution show better performance than the Kalman filter.

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Leishman, Robert C., Timothy W. McLain, and Randal W. Beard. "Relative Navigation Approach for Vision-Based Aerial GPS-Denied Navigation." Journal of Intelligent & Robotic Systems 74, no. 1-2 (October 9, 2013): 97–111. http://dx.doi.org/10.1007/s10846-013-9914-7.

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Bachrach, Abraham, Samuel Prentice, Ruijie He, and Nicholas Roy. "RANGE-Robust autonomous navigation in GPS-denied environments." Journal of Field Robotics 28, no. 5 (August 9, 2011): 644–66. http://dx.doi.org/10.1002/rob.20400.

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SUZUKI, Satoshi, Hongkyu MIN, Tetsuya WADA, and Kenzo NONAMI. "Integrated Navigation of Aerial Robot in GPS and GPS-denied Environment." Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec) 2016 (2016): 1P1–10a2. http://dx.doi.org/10.1299/jsmermd.2016.1p1-10a2.

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Suzuki, Satoshi, Hongkyu Min, Tetsuya Wada, and Kenzo Nonami. "Integrated navigation of aerial robot for GPS and GPS-denied environment." Journal of Physics: Conference Series 744 (September 2016): 012219. http://dx.doi.org/10.1088/1742-6596/744/1/012219.

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Soldado Serrano, Inmaculada, José García Doblado, Ignazio Federico Finazzi, and María Ángeles Martín Prats. "Simless GSM positioning for navigation in GPS-denied environments." Aircraft Engineering and Aerospace Technology 90, no. 7 (October 1, 2018): 1072–76. http://dx.doi.org/10.1108/aeat-01-2017-0029.

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Purpose Until now, air navigation systems have mainly relied on global navigation satellite systems (GNSS) on the worldwide spread global positioning system. However, the so-called GNSS-denied environments open a new research line which pursues the development of alternative technologies which will cover this gap in positioning systems’ services. Design/methodology/approach This paper presents the possibility of using positioning systems based on global system for mobile communications (GSM) signal. This approach developed in a standalone device will provide real-time information. The presented algorithm pursues a new methodology for providing useful information. Findings Among all the different technologies aimed at giving a navigation solution in the absence of any kind of GNSS in which this paper is based, it advocates for the use of the signals of opportunity, particularly the usage of GSM. Practical implications Technology is currently immersed in an era of continuous progress and expansion of navigation systems. These are evolving toward high performance systems, offering precise, efficient and safe air navigation. In addition, the growing demand for unmanned aerial vehicles increases the level of exigency on this activity even more. Therefore, in the context of the development of unmanned navigation technologies, the aim is to implement positioning systems that will allow high precision even in though environments. Originality/value Referencing the SIMless concept, a SIM-free system is described in this paper. The SIM-free system is supported by open data bases and permits the positioning based on the information sniffed from the signals broadcast by a set of several nearby base station of the GSM network. Hence, it provides same and in some cases even better accuracies than the already developed techniques, not being necessary to synchronize the link between the mobile terminal and the base station transceiver (BTS).

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Aftatah, Mohammed, Abdelkabir Lahrech, Abdelouahed Abounada, and Aziz Soulhi. "GPS/INS/Odometer Data Fusion for Land Vehicle Localization in GPS Denied Environment." Modern Applied Science 11, no. 1 (October 11, 2016): 62. http://dx.doi.org/10.5539/mas.v11n1p62.

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The main purpose of this paper is to present a fusion approach to bridge the period of Global Positioning System (GPS) outages using two proprioceptive sensors that are the Inertial Navigation System (INS) and the odometer in order to assure a continuous localization for land vehicle in urban areas where GPS signal blockage is very often. Odometer and GPS measures are exploited to correct inertial sensor errors. In fact, during GPS availability, INS is integrated with GPS to provide accurate localization solution; whereas during GPS outages, the odometer measurements are used to correct the INS error thereby improving the positioning accuracy and assuring the continuity of navigation solution. The problem of estimation of vehicle localization is realized by Kalman Filter (KF) that merges sensor measurements. The paper thus introduces results from simulation and real data.

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Ali, Abdelrahman, and Naser El-Sheimy. "Low-Cost MEMS-Based Pedestrian Navigation Technique for GPS-Denied Areas." Journal of Sensors 2013 (2013): 1–10. http://dx.doi.org/10.1155/2013/197090.

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The progress in the micro electro mechanical system (MEMS) sensors technology in size, cost, weight, and power consumption allows for new research opportunities in the navigation field. Today, most of smartphones, tablets, and other handheld devices are fully packed with the required sensors for any navigation system such as GPS, gyroscope, accelerometer, magnetometer, and pressure sensors. For seamless navigation, the sensors’ signal quality and the sensors availability are major challenges. Heading estimation is a fundamental challenge in the GPS-denied environments; therefore, targeting accurate attitude estimation is considered significant contribution to the overall navigation error. For that end, this research targets an improved pedestrian navigation by developing sensors fusion technique to exploit the gyroscope, magnetometer, and accelerometer data for device attitude estimation in the different environments based on quaternion mechanization. Results indicate that the improvement in the traveled distance and the heading estimations is capable of reducing the overall position error to be less than 15 m in the harsh environments.

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Perez-Grau, Francisco J., Ricardo Ragel, Fernando Caballero, Antidio Viguria, and Anibal Ollero. "An architecture for robust UAV navigation in GPS-denied areas." Journal of Field Robotics 35, no. 1 (October 11, 2017): 121–45. http://dx.doi.org/10.1002/rob.21757.

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Ellingson, Gary, Kevin Brink, and Tim McLain. "Relative navigation of fixed‐wing aircraft in GPS‐denied environments." NAVIGATION 67, no. 2 (June 2020): 255–73. http://dx.doi.org/10.1002/navi.364.

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Koch, Daniel P., David O. Wheeler, Randal W. Beard, Timothy W. McLain, and Kevin M. Brink. "Relative multiplicative extended Kalman filter for observable GPS-denied navigation." International Journal of Robotics Research 39, no. 9 (June 23, 2020): 1085–121. http://dx.doi.org/10.1177/0278364920903094.

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This work presents a multiplicative extended Kalman filter (MEKF) for estimating the relative state of a multirotor vehicle operating in a GPS-denied environment. The filter fuses data from an inertial measurement unit and altimeter with relative-pose updates from a keyframe-based visual odometry or laser scan-matching algorithm. Because the global position and heading states of the vehicle are unobservable in the absence of global measurements such as GPS, the filter in this article estimates the state with respect to a local frame that is colocated with the odometry keyframe. As a result, the odometry update provides nearly direct measurements of the relative vehicle pose, making those states observable. Recent publications have rigorously documented the theoretical advantages of such an observable parameterization, including improved consistency, accuracy, and system robustness, and have demonstrated the effectiveness of such an approach during prolonged multirotor flight tests. This article complements this prior work by providing a complete, self-contained, tutorial derivation of the relative MEKF, which has been thoroughly motivated but only briefly described to date. This article presents several improvements and extensions to the filter while clearly defining all quaternion conventions and properties used, including several new useful properties relating to error quaternions and their Euler-angle decomposition. Finally, this article derives the filter both for traditional dynamics defined with respect to an inertial frame, and for robocentric dynamics defined with respect to the vehicle’s body frame, and provides insights into the subtle differences that arise between the two formulations.

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Gupta, Ashish, Huan Chang, and Alper Yilmaz. "GPS-DENIED GEO-LOCALISATION USING VISUAL ODOMETRY." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences III-3 (June 3, 2016): 263–70. http://dx.doi.org/10.5194/isprsannals-iii-3-263-2016.

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The primary method for geo-localization is based on GPS which has issues of localization accuracy, power consumption, and unavailability. This paper proposes a novel approach to geo-localization in a GPS-denied environment for a mobile platform. Our approach has two principal components: public domain transport network data available in GIS databases or OpenStreetMap; and a trajectory of a mobile platform. This trajectory is estimated using visual odometry and 3D view geometry. The transport map information is abstracted as a graph data structure, where various types of roads are modelled as graph edges and typically intersections are modelled as graph nodes. A search for the trajectory in real time in the graph yields the geo-location of the mobile platform. Our approach uses a simple visual sensor and it has a low memory and computational footprint. In this paper, we demonstrate our method for trajectory estimation and provide examples of geolocalization using public-domain map data. With the rapid proliferation of visual sensors as part of automated driving technology and continuous growth in public domain map data, our approach has the potential to completely augment, or even supplant, GPS based navigation since it functions in all environments.

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Gupta, Ashish, Huan Chang, and Alper Yilmaz. "GPS-DENIED GEO-LOCALISATION USING VISUAL ODOMETRY." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences III-3 (June 3, 2016): 263–70. http://dx.doi.org/10.5194/isprs-annals-iii-3-263-2016.

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The primary method for geo-localization is based on GPS which has issues of localization accuracy, power consumption, and unavailability. This paper proposes a novel approach to geo-localization in a GPS-denied environment for a mobile platform. Our approach has two principal components: public domain transport network data available in GIS databases or OpenStreetMap; and a trajectory of a mobile platform. This trajectory is estimated using visual odometry and 3D view geometry. The transport map information is abstracted as a graph data structure, where various types of roads are modelled as graph edges and typically intersections are modelled as graph nodes. A search for the trajectory in real time in the graph yields the geo-location of the mobile platform. Our approach uses a simple visual sensor and it has a low memory and computational footprint. In this paper, we demonstrate our method for trajectory estimation and provide examples of geolocalization using public-domain map data. With the rapid proliferation of visual sensors as part of automated driving technology and continuous growth in public domain map data, our approach has the potential to completely augment, or even supplant, GPS based navigation since it functions in all environments.

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Atia, Mohamed Maher, Tashfeen Karamat, and Aboelmagd Noureldin. "An Enhanced 3D Multi-Sensor Integrated Navigation System for Land-Vehicles." Journal of Navigation 67, no. 4 (March 12, 2014): 651–71. http://dx.doi.org/10.1017/s0373463314000083.

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In urban areas, Global Positioning System (GPS) accuracy deteriorates due to signal degradation and multipath effects. To provide accurate and robust navigation in such GPS-denied environments, multi-sensor integrated navigation systems are developed. This paper introduces a 3D multi-sensor navigation system that integrates inertial sensors, odometry and GPS for land-vehicle navigation. A new error model is developed and an efficient loosely coupled closed-loop Kalman Filter (Extended KF or EKF) integration scheme is proposed. In this EKF-based integration scheme, the inertial/odometry navigation output is continuously corrected by EKF-estimated errors, which keeps the errors within acceptable linearization ranges which improves the prediction accuracy of the linearized dynamic error model. Consequently, the overall performance of the integrated system is improved. Real road experiments and comparison with earlier works have demonstrated a more reliable performance during GPS signal degradation and accurate estimation of inertial sensor errors (biases) have led to a more sustainable performance reliability during long GPS complete outages.

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Shen, Chong, Zesen Bai, Huiliang Cao, Ke Xu, Chenguang Wang, Huaiyu Zhang, Ding Wang, Jun Tang, and Jun Liu. "Optical Flow Sensor/INS/Magnetometer Integrated Navigation System for MAV in GPS-Denied Environment." Journal of Sensors 2016 (2016): 1–10. http://dx.doi.org/10.1155/2016/6105803.

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The drift of inertial navigation system (INS) will lead to large navigation error when a low-cost INS is used in microaerial vehicles (MAV). To overcome the above problem, an INS/optical flow/magnetometer integrated navigation scheme is proposed for GPS-denied environment in this paper. The scheme, which is based on extended Kalman filter, combines INS and optical flow information to estimate the velocity and position of MAV. The gyro, accelerator, and magnetometer information are fused together to estimate the MAV attitude when the MAV is at static state or uniformly moving state; and the gyro only is used to estimate the MAV attitude when the MAV is accelerating or decelerating. The MAV flight data is used to verify the proposed integrated navigation scheme, and the verification results show that the proposed scheme can effectively reduce the errors of navigation parameters and improve navigation precision.

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Bai, He, and Clark N. Taylor. "Future Uncertainty-Based Control for Relative Navigation in GPS-Denied Environments." IEEE Transactions on Aerospace and Electronic Systems 56, no. 5 (October 2020): 3491–501. http://dx.doi.org/10.1109/taes.2020.2974052.

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Wang, Teng, Koray Celik, and Arun K. Somani. "Characterization of mountain drainage patterns for GPS-denied UAS navigation augmentation." Machine Vision and Applications 27, no. 1 (October 26, 2015): 87–101. http://dx.doi.org/10.1007/s00138-015-0723-9.

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Nakanishi, Hiroaki, and Hiroyuki Hashimoto. "AR-Marker/IMU Hybrid Navigation System for Tether-Powered UAV." Journal of Robotics and Mechatronics 30, no. 1 (February 20, 2018): 76–85. http://dx.doi.org/10.20965/jrm.2018.p0076.

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Electrically powered unmanned aerial vehicles (UAV) are useful in performing inspection at various infrastructures or plants. A power supply through a tether cable is effective in extending flight time. During inspection activities, some or all satellites may be occluded. UAVs for inspection must be operated even in GPS-denied areas; therefore, a navigation system for GPS-denied areas is required. Depth information cannot be obtained correctly by a monocular camera. The ARToolkit, which is widely applied in augmented reality (AR), is not sufficient as a UAV navigation system. We have proposed a hybrid navigation method that integrates the ARToolkit and an inertial measurement unit (IMU). An analytic solution for both the worst and best estimation of yaw angle can be obtained by simple computation and helps remove outliers in measurements. From experimental results, it was proven that position estimation using the proposed method corresponded reasonably; however, it was necessary to correct the difference between the camera origin and the body’s center of gravity.

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Talmadge, Ramsay, Luke Jenkins, Drew Hidalgo, Josh Olivas, and Roger Burk. "Alternatives for Navigating Small Unmanned Air Vehicles without GPS." Industrial and Systems Engineering Review 4, no. 2 (November 12, 2016): 96–113. http://dx.doi.org/10.37266/iser.2016v4i2.pp96-113.

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Considering the increased reliance on GPS navigation for the Army’s Unmanned Aircraft Systems, adversaries have invested in capabilities to deny our systems access to genuine GPS signals. Although significant effort has been put forth in the areas of anti-jamming and anti-spoofing in GPS receivers, a need for alternative navigation methods in a GPS denied environment has grown in importance. This report outlines the recommendation and analysis completed for Mr. Lars Ericsson of the Army Project Manager Unmanned Aircraft Systems (PM-UAS). The report includes background research in the domain space, comprehensive stakeholder analysis, derived system requirements and functional requirements, ending with alternative generation, value scoring, costing, and provided findings for a recommended alternative for future consideration.

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Silva Filho, P., E. H. Shiguemori, and O. Saotome. "UAV VISUAL AUTOLOCALIZATON BASED ON AUTOMATIC LANDMARK RECOGNITION." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences IV-2/W3 (August 18, 2017): 89–94. http://dx.doi.org/10.5194/isprs-annals-iv-2-w3-89-2017.

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Deploying an autonomous unmanned aerial vehicle in GPS-denied areas is a highly discussed problem in the scientific community. There are several approaches being developed, but the main strategies yet considered are computer vision based navigation systems. This work presents a new real-time computer-vision position estimator for UAV navigation. The estimator uses images captured during flight to recognize specific, well-known, landmarks in order to estimate the latitude and longitude of the aircraft. The method was tested in a simulated environment, using a dataset of real aerial images obtained in previous flights, with synchronized images, GPS and IMU data. The estimated position in each landmark recognition was compatible with the GPS data, stating that the developed method can be used as an alternative navigation system.

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Liu, Fei, Houzeng Han, Xin Cheng, and Binghao Li. "Performance of Tightly Coupled Integration of GPS/BDS/MEMS-INS/Odometer for Real-Time High-Precision Vehicle Positioning in Urban Degraded and Denied Environment." Journal of Sensors 2020 (February 28, 2020): 1–15. http://dx.doi.org/10.1155/2020/8670262.

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Global Navigation Satellite System Real-Time Kinematic (GNSS-RTK) technology is widely used in vehicle navigation, but in complex environments such as urban high-rise street, wooded street, overpass, and tunnel, satellite signals are prone to attenuation or even unavailability. It brings great challenges to the continuous high-precision navigation. For this reason, a tightly coupled (TC) integration algorithm for GPS (Global Positioning System)/BDS (BeiDou Navigation Satellite System)/MEMS-INS (Micro-Electro-Mechanical System-Inertial Navigation System)/Odometer (GCIO) is proposed for vehicle navigation in complex urban environments. The accuracy improvement and ambiguity resolution (AR) performance are analysed in this research. First of all, the INS positioning error is constrained by fusion GPS/BDS (GC) and odometer; then, the predicted position information is used to aid GPS/BDS ambiguity resolution. In GNSS-denied environments, the odometer/INS integration is still carried out for continuous navigation. Real-time experiments are carried out in urban degraded and denied environments to validate the performance of the integrated system. In high-rise streets, the ambiguity fixing success rate of GCIO mode is 13.57% higher than that of GC mode. In the wooded street environment, the success rate has increased particularly significantly, by about 55 percent. The positioning accuracy analysis for open environment, high-rise street, wooded street, overpass, and tunnel is conducted. The experimental results show that in the above environment, the order of 0.1 m positioning accuracy can be achieved in the case of satellite outage for 1 minute, which can meet the positioning needs in most scenarios.

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Yang, Qifeng, Daokui Qu, Fang Xu, Fengshan Zou, Guojian He, and Mingze Sun. "Mobile robot motion control and autonomous navigation in GPS-denied outdoor environments using 3D laser scanning." Assembly Automation 39, no. 3 (August 5, 2019): 469–78. http://dx.doi.org/10.1108/aa-02-2018-029.

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Purpose This paper aims to propose a series of approaches to solve the problem of the mobile robot motion control and autonomous navigation in large-scale outdoor GPS-denied environments. Design/methodology/approach Based on the model of mobile robot with two driving wheels, a controller is designed and tested in obstacle-cluttered scenes in this paper. By using the priori “topology-geometry” map constructed based on the odometer data and the online matching algorithm of 3D-laser scanning points, a novel approach of outdoor localization with 3D-laser scanner is proposed to solve the problem of poor localization accuracy in GPS-denied environments. A path planning strategy based on geometric feature analysis and priority evaluation algorithm is also adopted to ensure the safety and reliability of mobile robot’s autonomous navigation and control. Findings A series of experiments are conducted with a self-designed mobile robot platform in large-scale outdoor environments, and the experimental results show the validity and effectiveness of the proposed approach. Originality/value The problem of motion control for a differential drive mobile robot is investigated in this paper first. At the same time, a novel approach of outdoor localization with 3D-laser scanner is proposed to solve the problem of poor localization accuracy in GPS-denied environments. A path planning strategy based on geometric feature analysis and priority evaluation algorithm is also adopted to ensure the safety and reliability of mobile robot’s autonomous navigation and control.

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Chen, Zhongyuan, Wanchun Chen, Xiaoming Liu, and Chuang Song. "Fault-Tolerant Optical Flow Sensor/SINS Integrated Navigation Scheme for MAV in a GPS-Denied Environment." Journal of Sensors 2018 (September 20, 2018): 1–17. http://dx.doi.org/10.1155/2018/9678505.

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An integrated navigation scheme based on multiple optical flow sensors and a strapdown inertial navigation system (SINS) are presented, instead of the global position system (GPS) aided. Multiple optical flow sensors are mounted on a micro air vehicle (MAV) at different positions with different viewing directions for detecting optical flow around the MAV. A fault-tolerant decentralized extended Kalman filter (EKF) is performed for estimating navigation errors by fusing the inertial and optical flow measurements, which can prevent the estimation divergence caused by the failure of the optical flow sensor. Then, the estimation of navigation error is inputted into the SINS settlement process for correcting the SINS measurements. The results verify that the navigation errors of SINS can be effectively reduced (even more than 9/10). Moreover, although the sensor is in a state of failure for 400 seconds, the fault-tolerant integrated navigation system can still work properly without divergence.

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Qin, M., X. Wan, Y. Y. Shao, and S. Y. Li. "AN AUTONOMOUS GPS-DENIED UNMANNED VEHICLE PLATFORM BASED ON BINOCULAR VISION FOR PLANETARY EXPLORATION." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3 (April 30, 2018): 1439–45. http://dx.doi.org/10.5194/isprs-archives-xlii-3-1439-2018.

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Vision-based navigation has become an attractive solution for autonomous navigation for planetary exploration. This paper presents our work of designing and building an autonomous vision-based GPS-denied unmanned vehicle and developing an ARFM (Adaptive Robust Feature Matching) based VO (Visual Odometry) software for its autonomous navigation. The hardware system is mainly composed of binocular stereo camera, a pan-and tilt, a master machine, a tracked chassis. And the ARFM-based VO software system contains four modules: camera calibration, ARFM-based 3D reconstruction, position and attitude calculation, BA (Bundle Adjustment) modules. Two VO experiments were carried out using both outdoor images from open dataset and indoor images captured by our vehicle, the results demonstrate that our vision-based unmanned vehicle is able to achieve autonomous localization and has the potential for future planetary exploration.

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Weiss, Stephan, Davide Scaramuzza, and Roland Siegwart. "Monocular-SLAM-based navigation for autonomous micro helicopters in GPS-denied environments." Journal of Field Robotics 28, no. 6 (October 12, 2011): 854–74. http://dx.doi.org/10.1002/rob.20412.

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ATIA, M. M., A. NOURELDIN, J. GEORGY, and M. KORENBERG. "Bayesian Filtering Based WiFi/INS Integrated Navigation Solution for GPS-Denied Environments." Navigation 58, no. 2 (June 2011): 111–25. http://dx.doi.org/10.1002/j.2161-4296.2011.tb01795.x.

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Badshah, Amir, Naveed Islam, Danish Shahzad, Bilal Jan, Haleem Farman, Murad Khan, Gwanggil Jeon, and Awais Ahmad. "Vehicle navigation in GPS denied environment for smart cities using vision sensors." Computers, Environment and Urban Systems 77 (September 2019): 101281. http://dx.doi.org/10.1016/j.compenvurbsys.2018.09.001.

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Urzua, Sarquis, Rodrigo Munguía, and Antoni Grau. "Vision-based SLAM system for MAVs in GPS-denied environments." International Journal of Micro Air Vehicles 9, no. 4 (June 6, 2017): 283–96. http://dx.doi.org/10.1177/1756829317705325.

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Using a camera, a micro aerial vehicle (MAV) can perform visual-based navigation in periods or circumstances when GPS is not available, or when it is partially available. In this context, the monocular simultaneous localization and mapping (SLAM) methods represent an excellent alternative, due to several limitations regarding to the design of the platform, mobility and payload capacity that impose considerable restrictions on the available computational and sensing resources of the MAV. However, the use of monocular vision introduces some technical difficulties as the impossibility of directly recovering the metric scale of the world. In this work, a novel monocular SLAM system with application to MAVs is proposed. The sensory input is taken from a monocular downward facing camera, an ultrasonic range finder and a barometer. The proposed method is based on the theoretical findings obtained from an observability analysis. Experimental results with real data confirm those theoretical findings and show that the proposed method is capable of providing good results with low-cost hardware.

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Wang, Tianmiao, Chaolei Wang, Jianhong Liang, Yang Chen, and Yicheng Zhang. "Vision-Aided Inertial Navigation for Small Unmanned Aerial Vehicles in GPS-Denied Environments." International Journal of Advanced Robotic Systems 10, no. 6 (January 2013): 276. http://dx.doi.org/10.5772/56660.

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S Warrier, Sushmita, and Hardik Modi. "Comparative Study of Navigation Methods for Unmanned Vehicles in a GPS-Denied Environment." International Journal of Wireless and Microwave Technologies 6, no. 2 (March 8, 2016): 30–38. http://dx.doi.org/10.5815/ijwmt.2016.02.04.

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Voro, Shlomi, and Itamar Lavidor. "Seamless Navigation in GPS-denied environment using Lirhot’s Real time North finding sensor." Annual of Navigation 26, no. 1 (December 1, 2019): 92–97. http://dx.doi.org/10.1515/aon-2019-0009.

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Abstract This paper presents a new type of north finding sensor. The passive optical sensor captures images of the sky at a high frame rate and analyzes them into a polarized map of the sky with a high degree of accuracy. The sensor operates in real time, under various weather and atmospheric conditions. The sensor output shows high heading accuracy relative to the celestial true north. Based upon the NAS-14V2 astronomical method of navigation, it is possible to define the sensor global position on earth.

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El-Diasty, Mohammed, and Spiros Pagiatakis. "A Frequency-Domain INS/GPS Dynamic Response Method for Bridging GPS Outages." Journal of Navigation 63, no. 4 (September 13, 2010): 627–43. http://dx.doi.org/10.1017/s0373463310000226.

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We develop a new frequency-domain dynamic response method to model integrated Inertial Navigation System (INS) and Global Positioning System (GPS) architectures and provide an accurate impulse-response-based INS-only navigation solution when GPS signals are denied (GPS outages). The input to such a dynamic system is the INS-only solution and the output is the INS/GPS integration solution; both are used to derive the transfer function of the dynamic system using Least Squares Frequency Transform (LSFT). The discrete Inverse Least Squares Frequency Transform (ILSFT) of the transfer function is applied to estimate the impulse response of the INS/GPS system in the time domain. It is shown that the long-term motion dynamics of a DQI-100 IMU/Trimble BD950 integrated system are recovered by 72%, 42%, 75%, and 40% for north and east velocities, and north and east positions respectively, when compared with the INS-only solution (prediction mode of the INS/GPS filter). A comparison between our impulse response model and the current state-of-the-art time-domain feed-forward neural network shows that the proposed frequency-dependent INS/GPS response model is superior to the neural network model by about 26% for 2D velocities and positions during GPS outages.

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Zhang, Lei, Zhengjun Zhai, Lang He, Pengcheng Wen, and Wensheng Niu. "Infrared-Inertial Navigation for Commercial Aircraft Precision Landing in Low Visibility and GPS-Denied Environments." Sensors 19, no. 2 (January 20, 2019): 408. http://dx.doi.org/10.3390/s19020408.

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This paper proposes a novel infrared-inertial navigation method for the precise landing of commercial aircraft in low visibility and Global Position System (GPS)-denied environments. Within a Square-root Unscented Kalman Filter (SR_UKF), inertial measurement unit (IMU) data, forward-looking infrared (FLIR) images and airport geo-information are integrated to estimate the position, velocity and attitude of the aircraft during landing. Homography between the synthetic image and the real image which implicates the camera pose deviations is created as vision measurement. To accurately extract real runway features, the current results of runway detection are used as the prior knowledge for the next frame detection. To avoid possible homography decomposition solutions, it is directly converted to a vector and fed to the SR_UKF. Moreover, the proposed navigation system is proven to be observable by nonlinear observability analysis. Last but not least, a general aircraft was elaborately equipped with vision and inertial sensors to collect flight data for algorithm verification. The experimental results have demonstrated that the proposed method could be used for the precise landing of commercial aircraft in low visibility and GPS-denied environments.

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Vanegas, Fernando, and Felipe Gonzalez. "Enabling UAV Navigation with Sensor and Environmental Uncertainty in Cluttered and GPS-Denied Environments." Sensors 16, no. 5 (May 10, 2016): 666. http://dx.doi.org/10.3390/s16050666.

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Chowdhary, Girish, Eric N. Johnson, Daniel Magree, Allen Wu, and Andy Shein. "GPS-denied Indoor and Outdoor Monocular Vision Aided Navigation and Control of Unmanned Aircraft." Journal of Field Robotics 30, no. 3 (March 19, 2013): 415–38. http://dx.doi.org/10.1002/rob.21454.

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Atia, M. M., M. J. Korenberg, and A. Noureldin. "Particle-Filter-Based WiFi-Aided Reduced Inertial Sensors Navigation System for Indoor and GPS-Denied Environments." International Journal of Navigation and Observation 2012 (June 25, 2012): 1–12. http://dx.doi.org/10.1155/2012/753206.

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Indoor navigation is challenging due to unavailability of satellites-based signals indoors. Inertial Navigation Systems (INSs) may be used as standalone navigation indoors. However, INS suffers from growing drifts without bounds due to error accumulation. On the other side, the IEEE 802.11 WLAN (WiFi) is widely adopted which prompted many researchers to use it to provide positioning indoors using fingerprinting. However, due to WiFi signal noise and multipath errors indoors, WiFi positioning is scattered and noisy. To benefit from both WiFi and inertial systems, in this paper, two major techniques are applied. First, a low-cost Reduced Inertial Sensors System (RISS) is integrated with WiFi to smooth the noisy scattered WiFi positioning and reduce RISS drifts. Second, a fast feature reduction technique is applied to fingerprinting to identify the WiFi access points with highest discrepancy power to be used for positioning. The RISS/WiFi system is implemented using a fast version of Mixture Particle Filter for state estimation as nonlinear non-Gaussian filtering algorithm. Real experiments showed that drifts of RISS are greatly reduced and the scattered noisy WiFi positioning is significantly smoothed. The proposed system provides smooth indoor positioning of 1 m accuracy 70% of the time outperforming each system individually.

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Jayasiri, Awantha, Raymond G. Gosine, George K. I. Mann, and Peter McGuire. "AUV-Based Plume Tracking: A Simulation Study." Journal of Control Science and Engineering 2016 (2016): 1–15. http://dx.doi.org/10.1155/2016/1764527.

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This paper presents a simulation study of an autonomous underwater vehicle (AUV) navigation system operating in a GPS-denied environment. The AUV navigation method makes use of underwater transponder positioning and requires only one transponder. A multirate unscented Kalman filter is used to determine the AUV orientation and position by fusing high-rate sensor data and low-rate information. The paper also proposes a gradient-based, efficient, and adaptive novel algorithm for plume boundary tracking missions. The algorithm follows a centralized approach and it includes path optimization features based on gradient information. The proposed algorithm is implemented in simulation on the AUV-based navigation system and successful boundary tracking results are obtained.

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Çelik, Koray, and Arun K. Somani. "Monocular Vision SLAM for Indoor Aerial Vehicles." Journal of Electrical and Computer Engineering 2013 (2013): 1–15. http://dx.doi.org/10.1155/2013/374165.

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This paper presents a novel indoor navigation and ranging strategy via monocular camera. By exploiting the architectural orthogonality of the indoor environments, we introduce a new method to estimate range and vehicle states from a monocular camera for vision-based SLAM. The navigation strategy assumes an indoor or indoor-like manmade environment whose layout is previously unknown, GPS-denied, representable via energy based feature points, and straight architectural lines. We experimentally validate the proposed algorithms on a fully self-contained microaerial vehicle (MAV) with sophisticated on-board image processing and SLAM capabilities. Building and enabling such a small aerial vehicle to fly in tight corridors is a significant technological challenge, especially in the absence of GPS signals and with limited sensing options. Experimental results show that the system is only limited by the capabilities of the camera and environmental entropy.

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Sinha, Abhijit, Svetlana Stolpner, Abir Mukherjee, and Simon Monckton. "A Precise State Transition Model for Aircraft Navigation." GEOMATICA 68, no. 4 (December 2014): 283–97. http://dx.doi.org/10.5623/cig2014-403.

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This article considers the problem of accurately modeling the kinematic state transition of an Unmanned Aerial Vehicle (UAV). The full 3D range of motion is accurately captured using compact equations for position update derived in this work. This derivation makes use of the independence of the rotation and translation components of a 3D rigid motion. The proposed motion model is transparent to the sensors used in the system; it is particularly useful in GPS-denied environments and can contribute to different aspects of robust navigation, such as accurate state estimation, sensor fault tolerance and sensor bias estimation. Experimental results comparing the performance of the proposed kinematic model with those typically used demonstrate its superiority.

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Kim, Youngjoo, Wooyoung Jung, and Hyochoong Bang. "Visual Target Tracking and Relative Navigation for Unmanned Aerial Vehicles in a GPS-Denied Environment." International Journal of Aeronautical and Space Sciences 15, no. 3 (September 30, 2014): 258–66. http://dx.doi.org/10.5139/ijass.2014.15.3.258.

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Saeedi, Sajad, Carl Thibault, Michael Trentini, and Howard Li. "3D Mapping for Autonomous Quadrotor Aircraft." Unmanned Systems 05, no. 03 (July 2017): 181–96. http://dx.doi.org/10.1142/s2301385017400064.

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Autonomous navigation in global positioning system (GPS)-denied environments is one of the challenging problems in robotics. For small flying robots, autonomous navigation is even more challenging. These robots have limitations such as fast dynamics and limited sensor payload. To develop an autonomous robot, many challenges including two-dimensional (2D) and three-dimensional (3D) perception, path planning, exploration, and obstacle avoidance should be addressed in real-time and with limited resources. In this paper, a complete solution for autonomous navigation of a quadrotor rotorcraft is presented. The proposed solution includes 2D and 3D mapping with several autonomous behaviors such as target localization and displaying maps on multiple remote tablets. Multiple tests were performed in simulated and indoor/outdoor environments to show the effectiveness of the proposed solution.

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Lee, Dongjin, Youngjoo Kim, and Hyochoong Bang. "Vision-aided terrain referenced navigation for unmanned aerial vehicles using ground features." Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering 228, no. 13 (January 6, 2014): 2399–413. http://dx.doi.org/10.1177/0954410013517804.

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A vision-aided terrain referenced navigation (VATRN) approach is addressed for autonomous navigation of unmanned aerial vehicles (UAVs) under GPS-denied conditions. A typical terrain referenced navigation (TRN) algorithm blends inertial navigation data with measured terrain information to estimate vehicle’s position. In this paper, a low-cost inertial navigation system (INS) for UAVs is supplemented with a monocular vision-aided navigation system and terrain height measurements. A point mass filter based on Bayesian estimation is employed as a TRN algorithm. Homograpies are established to estimate the vehicle’s relative translational motion using ground features with simple assumptions. And the error analysis in homography estimation is explored to estimate the error covariance matrix associated with the visual odometry data. The estimated error covariance is delivered to the TRN algorithm for robust estimation. Furthermore, multiple ground features tracked by image observations are utilized as multiple height measurements to improve the performance of the VATRN algorithm.

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Upadhyay, Jatin, Abhishek Rawat, and Dipankar Deb. "Multiple Drone Navigation and Formation Using Selective Target Tracking-Based Computer Vision." Electronics 10, no. 17 (September 1, 2021): 2125. http://dx.doi.org/10.3390/electronics10172125.

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Autonomous unmanned aerial vehicles work seamlessly within the GPS signal range, but their performance deteriorates in GPS-denied regions. This paper presents a unique collaborative computer vision-based approach for target tracking as per the image’s specific location of interest. The proposed method tracks any object without considering its properties like shape, color, size, or pattern. It is required to keep the target visible and line of sight during the tracking. The method gives freedom of selection to a user to track any target from the image and form a formation around it. We calculate the parameters like distance and angle from the image center to the object for the individual drones. Among all the drones, the one with a significant GPS signal strength or nearer to the target is chosen as the master drone to calculate the relative angle and distance between an object and other drones considering approximate Geo-location. Compared to actual measurements, the results of tests done on a quadrotor UAV frame achieve 99% location accuracy in a robust environment inside the exact GPS longitude and latitude block as GPS-only navigation methods. The individual drones communicate to the ground station through a telemetry link. The master drone calculates the parameters using data collected at ground stations. Various formation flying methods help escort other drones to meet the desired objective with a single high-resolution first-person view (FPV) camera. The proposed method is tested for Airborne Object Target Tracking (AOT) aerial vehicle model and achieves higher tracking accuracy.

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González-de Santos, L. M., J. Martínez-Sánchez, H. González-Jorge, A. Novo, and P. Arias. "FIRST APPROACH TO UAV-BASED CONTACT INSPECTION: A SMART PAYLOAD FOR NAVIGATION IN THE NEIGHBOURHOOD OF STRUCTURES." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W13 (June 4, 2019): 323–28. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w13-323-2019.

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<p><strong>Abstract.</strong> Many inspection tasks of structures are already carried out by unmanned aerial vehicles (UAV). Most of these inspections consist of using payloads for close range remote sensing purposes (i.e. digital cameras, thermal or LiDAR sensors). In all these inspection tasks the UAV system does not need to be close to the structure and typically the GPS coverage is good to perform mission navigation. In this paper, a smart payload developed for navigation in the neighbourhood of structures is presented. With this payload the UAV system is able to control the distance to a structure and the angle formed by the UAV and the structure in the horizontal plane. This payload has been calibrated in order to determine the calibration curve and measure the accuracy of the payload. The system has been tested in an indoor environment (GPS-denied). Good position and angular results has been obtained.</p>

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Chen, Chang, and Hua Zhu. "Visual-inertial SLAM method based on optical flow in a GPS-denied environment." Industrial Robot: An International Journal 45, no. 3 (May 21, 2018): 401–6. http://dx.doi.org/10.1108/ir-01-2018-0002.

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Purpose This study aims to present a visual-inertial simultaneous localization and mapping (SLAM) method for accurate positioning and navigation of mobile robots in the event of global positioning system (GPS) signal failure in buildings, trees and other obstacles. Design/methodology/approach In this framework, a feature extraction method distributes features on the image under texture-less scenes. The assumption of constant luminosity is improved, and the features are tracked by the optical flow to enhance the stability of the system. The camera data and inertial measurement unit data are tightly coupled to estimate the pose by nonlinear optimization. Findings The method is successfully performed on the mobile robot and steadily extracts the features on low texture environments and tracks features. The end-to-end error is 1.375 m with respect to the total length of 762 m. The authors achieve better relative pose error, scale and CPU load than ORB-SLAM2 on EuRoC data sets. Originality/value The main contribution of this study is the theoretical derivation and experimental application of a new visual-inertial SLAM method that has excellent accuracy and stability on weak texture scenes.

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Haque, Akkas, Ahmed Elsaharti, Tarek Elderini, Mohamed Atef Elsaharty, and Jeremiah Neubert. "UAV Autonomous Localization Using Macro-Features Matching with a CAD Model." Sensors 20, no. 3 (January 29, 2020): 743. http://dx.doi.org/10.3390/s20030743.

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Research in the field of autonomous Unmanned Aerial Vehicles (UAVs) has significantly advanced in recent years, mainly due to their relevance in a large variety of commercial, industrial, and military applications. However, UAV navigation in GPS-denied environments continues to be a challenging problem that has been tackled in recent research through sensor-based approaches. This paper presents a novel offline, portable, real-time in-door UAV localization technique that relies on macro-feature detection and matching. The proposed system leverages the support of machine learning, traditional computer vision techniques, and pre-existing knowledge of the environment. The main contribution of this work is the real-time creation of a macro-feature description vector from the UAV captured images which are simultaneously matched with an offline pre-existing vector from a Computer-Aided Design (CAD) model. This results in a quick UAV localization within the CAD model. The effectiveness and accuracy of the proposed system were evaluated through simulations and experimental prototype implementation. Final results reveal the algorithm’s low computational burden as well as its ease of deployment in GPS-denied environments.

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Manecy, Augustin, Nicolas Marchand, and Stéphane Viollet. "Hovering by Gazing: A Novel Strategy for Implementing Saccadic Flight-Based Navigation in GPS-Denied Environments." International Journal of Advanced Robotic Systems 11, no. 4 (April 23, 2014): 66. http://dx.doi.org/10.5772/58429.

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Aggarwal, Priyanka, David Thomas, Lauro Ojeda, and Johann Borenstein. "Map matching and heuristic elimination of gyro drift for personal navigation systems in GPS-denied conditions." Measurement Science and Technology 22, no. 2 (January 18, 2011): 025205. http://dx.doi.org/10.1088/0957-0233/22/2/025205.

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Eldesoky, Abdalla, Ahmed M. Kamel, M. Elhabiby, and Hadia Elhennawy. "Real time localization solution for land vehicle application using low-cost integrated sensors with GPS." Journal of Applied Research and Technology 18, no. 4 (August 30, 2020): 214–28. http://dx.doi.org/10.22201/icat.24486736e.2020.18.4.1196.

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The technique proposed in this research demonstrates a real time nonlinear data fusion solution based on extremely low-cost and grade inertial sensors for land vehicle navigation. Here, the utilized nonlinear multi-sensor data fusion (MSDF) is based on the combination between extremely low-cost micro electrical mechanical systems (MEMS) inertial, heading, pressure, and speed sensors in addition to satellite-based navigation system. The integrated navigation system fuses position and velocity states from the Global Positioning System (GPS), the velocity measurements from an odometer, heading angle observation from a magnetometer and navigation states from an inertial navigation system (INS). The implemented system performance is assessed through the post-processing of collected raw measurements and real time experimental work. The system that runs the real-time experiments is established on three connected platforms, two of them are based on a 32-bit ARMTM core and the third one is based 16-bit AVR ATMEL microcontroller. This microcontroller is connected to an on-board diagnostics (OBD) shield to collect the vehicle speed measurements. The raw data obtained from the integrated sensors is saved and post processed in MATLAB®. In normal conditions, the estimated position errors are reduced through the usage of INS/GPS integration with heading observation angle from a magnetometer. In GPS-denied environments, the integrated system uses the observations from INS, magnetometer in addition to the velocity from odometer to ensure a continuous and accurate navigation solution. A complementary filter (CF) is implemented to estimate and improve the pitch and roll angles calculations. In addition to that, an unscented Kalman filter (UKF) is used cascaded with the designed CF to complete the designed sensors fusion algorithm. Experimental results show that the designed MSDF can achieve a good level of accuracy and a continuous localization solution of a land vehicle in different GPS availability cases and can be implemented on the available in the market processors to be run in real time.

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Journal articles on the topic 'GPS-Denied navigation'

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