Fusion of Low-Cost Imaging and Inertial Sensors for Navigation PDF Download

Author:
Publisher:
ISBN:
Category :
Languages : en
Pages : 12

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Book Description
Aircraft navigation information (position, velocity, and attitude) can be determined using optical measurements from imaging sensors combined with an inertial navigation system. This can be accomplished by tracking the locations of optical features in multiple images and using the resulting geometry to estimate and remove inertial errors. A critical factor governing the performance of image-aided inertial navigation systems is the robustness of the feature tracking algorithm. Previous research has shown the strength of rigorously coupling the image and inertial sensors at the measurement level using a tactical-grade inertial sensor. While the tactical-grade inertial sensor is a reasonable choice for larger platforms, the greater physical size and cost of the sensor limits its use in smaller, low-cost platforms. In this paper, an image-aided inertial navigation algorithm is implemented using a multi-dimensional stochastic feature tracker. In contrast to previous research, the algorithms are specifically evaluated for operation using lowcost, CMOS imagers and MEMS inertial sensors. The performance of the resulting image-aided inertial navigation system is evaluated using Monte Carlo simulation and experimental data and compared to the performance using more expensive inertial sensors.

Author: Michael J. Veth
Publisher:
ISBN: 9780542834059
Category : Artificial satellites in navigation
Languages : en
Pages : 191

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Book Description
The introduction of the Global Positioning System changed the way the United States Air Force fights, by delivering world-wide, precision navigation capability to even the smallest platforms. Unfortunately, the Global Positioning System signal is not available in all combat environments (e.g., under tree cover, indoors, or underground). Thus, operations in these environments are limited to non-precision tactics. The motivation of this research is to address the limitations of the current precision navigation methods by fusing imaging and inertial systems, which is inspired by observing the navigation capabilities of animals. The research begins by rigorously describing the imaging and navigation problem and developing practical models of the sensors, then presenting a transformation technique to detect features within an image. Given a set of features, a rigorous, statistical feature projection technique is developed which utilizes inertial measurements to predict vectors in the feature space between images. This coupling of the imaging and inertial sensors at a deep level is then used to aid the statistical feature matching function. The feature matches and inertial measurements are then used to estimate the navigation trajectory online using an extended Kalman filter. After accomplishing a proper calibration, the image-aided inertial navigation algorithm is then tested using a combination of simulation and ground tests using both tactical and consumer-grade inertial sensors. While limitations of the extended Kalman filter are identified, the experimental results demonstrate a navigation performance improvement of at least two orders of magnitude over the respective inertial-only solutions.

Author: J. Fletcher
Publisher:
ISBN:
Category :
Languages : en
Pages : 13

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As evidenced by many biological systems, the fusion of optical and inertial sensors represents an attractive method for passive navigation. In our previous work, a rigorous theory for optical and inertial fusion was developed for precision navigation applications. The theory was based on a statistical transformation of the feature space based on inertial sensor measurements. The transformation effectively constrained the feature correspondence search to a given level of a priori statistical uncertainty. When integrated into a navigation system, the fused system demonstrated performance in indoor environments which were comparable to that of GPS-aided systems. In order to improve feature tracking performance, a robust feature transformation algorithm "Lowe?s SIFT" was chosen. The SIFT features are ideal for navigation applications in that they are invariant to scale, rotation, and illumination. Unfortunately, there exists a correlation between feature complexity and computer processing time. This limits the effectiveness of robust feature extraction algorithms for real-time applications using traditional microprocessor architectures. While recent advances in computer technology have made image processing more commonplace, the amount of information that can be processed is still limited by the power and speed of the CPU. In this paper, a new theory which exploits the highly parallel nature of General Programmable Graphical Processing Units "GPGPU" is developed which supports deeply integrated optical and inertial sensors for real-time navigation. Recent advances in GPGPU technology have made realtime, image-aided navigation a reality. Our approach leverages the existing OpenVIDIA core GPGPU library and commercially available computer hardware to solve the image and inertial fusion problem. The open-source libraries are extended to include the statistical featur.

Author: Jiadong Sun
Publisher: Springer Science & Business Media
ISBN: 3642291864
Category : Technology & Engineering
Languages : en
Pages : 679

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Proceedings of the 3rd China Satellite Navigation Conference (CSNC2012) presents selected research papers from CSNC2012, held on 15-19 May in Guanzhou, China. These papers discuss the technologies and applications of the Global Navigation Satellite System (GNSS), and the latest progress made in the China BeiDou system especially. They are divided into 9 topics to match the corresponding sessions in CSNC2012, which broadly covered key topics in GNSS. Readers can learn about the BeiDou system and keep abreast of the latest advances in GNSS techniques and applications. SUN Jiadong is the Chief Designer of the Compass/BeiDou system, and the Academician of Chinese Academy of Sciences; LIU Jingnan is a professor at Wuhan University, and the Academician of Chinese Academy of Engineering; YANG Yuanxi is a professor at China National Administration of GNSS and Applications, and the Academician of Chinese Academy of Sciences; FAN Shiwei is a researcher on satellite navigation.

Author: Nilesh Sharma Gopaul
Publisher:
ISBN:
Category :
Languages : en
Pages : 0

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The utilization of cameras in integrated navigation systems is among the most recent scientific research and high-tech industry development. The research is motivated by the requirement of calibrating off-the-shelf cameras and the fusion of imaging and inertial sensors in poor GNSS environments. The three major contributions of this dissertation are The development of a structureless camera auto-calibration and system calibration algorithm for a GNSS, IMU and stereo camera system. The auto-calibration bundle adjustment utilizes the scale restraint equation, which is free of object coordinates. The number of parameters to be estimated is significantly reduced in comparison with the ones in a self-calibrating bundle adjustment based on the collinearity equations. Therefore, the proposed method is computationally more efficient. The development of a loosely-coupled visual odometry aided inertial navigation algorithm. The fusion of the two sensors is usually performed using a Kalman filter. The pose changes are pairwise time-correlated, i.e. the measurement noise vector at the current epoch is only correlated with the one from the previous epoch. Time-correlated errors are usually modelled by a shaping filter. The shaping filter developed in this dissertation uses Cholesky factors as coefficients derived from the variance and covariance matrices of the measurement noise vectors. Test results with showed that the proposed algorithm performs better than the existing ones and provides more realistic covariance estimates. The development of a tightly-coupled stereo multi-frame aided inertial navigation algorithm for reducing position and orientation drifts. Usually, the image aiding based on the visual odometry uses the tracked features only from a pair of the consecutive image frames. The proposed method integrates the features tracked from multiple overlapped image frames for reducing the position and orientation drifts. The measurement equation is derived from SLAM measurement equation system where the landmark positions in SLAM are algebraically by time-differencing. However, the derived measurements are time-correlated. Through a sequential de-correlation, the Kalman filter measurement update can be performed sequentially and optimally. The main advantages of the proposed algorithm are the reduction of computational requirements when compared to SLAM and a seamless integration into an existing GNSS aided-IMU system.

Author: Vladimir L. Boginski
Publisher: Springer Science & Business Media
ISBN: 0387886192
Category : Mathematics
Languages : en
Pages : 245

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Book Description
The objective of this book is to advance the current knowledge of sensor research particularly highlighting recent advances, current work, and future needs. The goal is to share current technologies and steer future efforts in directions that will benefit the majority of researchers and practitioners working in this broad field of study.

Author: Dayi Wang
Publisher: Springer Nature
ISBN: 981154879X
Category : Technology & Engineering
Languages : en
Pages : 352

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Book Description
This book introduces readers to the fundamentals of estimation and dynamical system theory, and their applications in the field of multi-source information fused autonomous navigation for spacecraft. The content is divided into two parts: theory and application. The theory part (Part I) covers the mathematical background of navigation algorithm design, including parameter and state estimate methods, linear fusion, centralized and distributed fusion, observability analysis, Monte Carlo technology, and linear covariance analysis. In turn, the application part (Part II) focuses on autonomous navigation algorithm design for different phases of deep space missions, which involves multiple sensors, such as inertial measurement units, optical image sensors, and pulsar detectors. By concentrating on the relationships between estimation theory and autonomous navigation systems for spacecraft, the book bridges the gap between theory and practice. A wealth of helpful formulas and various types of estimators are also included to help readers grasp basic estimation concepts and offer them a ready-reference guide.

Author: S. Ebcin
Publisher:
ISBN:
Category :
Languages : en
Pages : 12

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Book Description
Accurate navigation information "position, velocity, and attitude" can be determined using optical measurements from imaging sensors combined with an inertial navigation system. This can be accomplished by tracking the locations of stationary optical features in multiple images and using the resulting geometry to estimate and remove inertial errors. In previous research efforts, we have demonstrated the effectiveness of fusing imaging and inertial sensors using an extended Kalman filter "EKF" algorithm. In this approach, the image feature correspondence search was aided using the inertial sensor measurements, resulting in more robust feature tracking. The resulting image-aided inertial algorithm was tested using both simulation and experimental data. While the tightly-coupled approach stabilized the feature correspondence search, the overall problem remained prone to filter divergence due to the well-known consequences of image scale ambiguity and the nonlinear measurement model. These effects are evidenced by the consistency divergence in the EKF implementation seen during our longdurationMonte-Carlo simulations. In other words, the measurement model is highly sensitive to the current parameter estimate, which invalidates the linearized measurement model assumed by the EKF. The unscented "sigma-point" Kalman filter "UKF" has been proposed in the literature in order to address the large class of recursive estimation problems which are not well-modeled using linearized dynamics and Gaussian noise models assumed in the EKF. The UKF leverages the unscented transformation in order to represent the state uncertainty using a set of carefully chosen sample points. This approach maintains mean and covariance estimates accurate to at least second order, by using the true nonlinear dynamics and measurement models. In this paper, a variation of the UKF is applied to the.

Author: Priyanka Aggarwal
Publisher: Artech House
ISBN: 1608070441
Category : Technology & Engineering
Languages : en
Pages : 213

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Due to their micro-scale size and low power consumption, Microelectromechanical systems (MEMS) are now being utilized in a variety of fields. This leading-edge resource focuses on the application of MEMS inertial sensors to navigation systems. The book shows you how to minimize cost by adding and removing inertial sensors. Moreover, this practical reference provides you with various integration strategies with examples from real field tests. From an introduction to MEMS navigation related applicationsOC to special topics on Alignment for MEMS-Based NavigationOC to discussions on the Extended Kalman Filter, this comprehensive book covers a wide range of critical topics in this fast-growing area."

Author: Andrei M. Shkel
Publisher: John Wiley & Sons
ISBN: 1119699894
Category : Technology & Engineering
Languages : en
Pages : 194

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Book Description
Explore an insightful summary of the major self-contained aiding technologies for pedestrian navigation from established and emerging leaders in the field Pedestrian Inertial Navigation with Self-Contained Aiding delivers a comprehensive and broad treatment of self-contained aiding techniques in pedestrian inertial navigation. The book combines an introduction to the general concept of navigation and major navigation and aiding techniques with more specific discussions of topics central to the field, as well as an exploration of the future of the future of the field: Ultimate Navigation Chip (uNavChip) technology. The most commonly used implementation of pedestrian inertial navigation, strapdown inertial navigation, is discussed at length, as are the mechanization, implementation, error analysis, and adaptivity of zero-velocity update aided inertial navigation algorithms. The book demonstrates the implementation of ultrasonic sensors, ultra-wide band (UWB) sensors, and magnetic sensors. Ranging techniques are considered as well, including both foot-to-foot ranging and inter-agent ranging, and learning algorithms, navigation with signals of opportunity, and cooperative localization are discussed. Readers will also benefit from the inclusion of: A thorough introduction to the general concept of navigation as well as major navigation and aiding techniques An exploration of inertial navigation implementation, Inertial Measurement Units, and strapdown inertial navigation A discussion of error analysis in strapdown inertial navigation, as well as the motivation of aiding techniques for pedestrian inertial navigation A treatment of the zero-velocity update (ZUPT) aided inertial navigation algorithm, including its mechanization, implementation, error analysis, and adaptivity Perfect for students and researchers in the field who seek a broad understanding of the subject, Pedestrian Inertial Navigation with Self-Contained Aiding will also earn a place in the libraries of industrial researchers and industrial marketing analysts who need a self-contained summary of the foundational elements of the field.