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Author: S. Ebcin Publisher: ISBN: Category : Languages : en Pages : 12
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: S. Ebcin Publisher: ISBN: Category : Languages : en Pages : 12
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: Michael G. Giebner Publisher: ISBN: 9781423502760 Category : Global Positioning System Languages : en Pages : 97
Book Description
Inertial navigation systems and GPS system's have revolutionized the world of navigation. Inertial system's are incapable of being jammed and are the backbone of most navigation system's. GPS is highly accurate over long periods of time, and it is an excellent aid to inertial navigation system's. However, as a military force we must be prepared to deal with the denial of the GPS signal. This thesis seeks to determine it, via simulation, it is viable to aid an INS with visual measurements.
Author: Nilesh Sharma Gopaul Publisher: ISBN: Category : Languages : en Pages : 0
Book Description
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: Jaydev P. Desai Publisher: Springer ISBN: 3319000659 Category : Technology & Engineering Languages : en Pages : 966
Book Description
The International Symposium on Experimental Robotics (ISER) is a series of bi-annual meetings, which are organized, in a rotating fashion around North America, Europe and Asia/Oceania. The goal of ISER is to provide a forum for research in robotics that focuses on novelty of theoretical contributions validated by experimental results. The meetings are conceived to bring together, in a small group setting, researchers from around the world who are in the forefront of experimental robotics research. This unique reference presents the latest advances across the various fields of robotics, with ideas that are not only conceived conceptually but also explored experimentally. It collects robotics contributions on the current developments and new directions in the field of experimental robotics, which are based on the papers presented at the 13the ISER held in Québec City, Canada, at the Fairmont Le Château Frontenac, on June 18-21, 2012. This present thirteenth edition of Experimental Robotics edited by Jaydev P. Desai, Gregory Dudek, Oussama Khatib, and Vijay Kumar offers a collection of a broad range of topics in field and human-centered robotics.
Author: Emilio Frazzoli Publisher: Springer ISBN: 3642362796 Category : Technology & Engineering Languages : en Pages : 625
Book Description
Algorithms are a fundamental component of robotic systems. Robot algorithms process inputs from sensors that provide noisy and partial data, build geometric and physical models of the world, plan high-and low-level actions at different time horizons, and execute these actions on actuators with limited precision. The design and analysis of robot algorithms raise a unique combination of questions from many elds, including control theory, computational geometry and topology, geometrical and physical modeling, reasoning under uncertainty, probabilistic algorithms, game theory, and theoretical computer science. The Workshop on Algorithmic Foundations of Robotics (WAFR) is a single-track meeting of leading researchers in the eld of robot algorithms. Since its inception in 1994, WAFR has been held every other year, and has provided one of the premiere venues for the publication of some of the eld's most important and lasting contributions. This books contains the proceedings of the tenth WAFR, held on June 13{15 2012 at the Massachusetts Institute of Technology. The 37 papers included in this book cover a broad range of topics, from fundamental theoretical issues in robot motion planning, control, and perception, to novel applications.
Author: Antonio Bicchi Publisher: Springer ISBN: 3319515322 Category : Technology & Engineering Languages : en Pages : 525
Book Description
ISRR, the "International Symposium on Robotics Research", is one of robotics pioneering Symposia, which has established over the past two decades some of the field's most fundamental and lasting contributions. This book presents the results of the seventeenth edition of "Robotics Research" ISRR15, offering a collection of a broad range of topics in robotics. The content of the contributions provides a wide coverage of the current state of robotics research.: the advances and challenges in its theoretical foundation and technology basis, and the developments in its traditional and new emerging areas of applications. The diversity, novelty, and span of the work unfolding in these areas reveal the field's increased maturity and expanded scope and define the state of the art of robotics and its future direction.
Author: Michael J. Veth Publisher: ISBN: 9780542834059 Category : Artificial satellites in navigation Languages : en Pages : 191
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: Aboelmagd Noureldin Publisher: Springer Science & Business Media ISBN: 3642304664 Category : Technology & Engineering Languages : en Pages : 324
Book Description
Fundamentals of Inertial Navigation, Satellite-based Positioning and their Integration is an introduction to the field of Integrated Navigation Systems. It serves as an excellent reference for working engineers as well as textbook for beginners and students new to the area. The book is easy to read and understand with minimum background knowledge. The authors explain the derivations in great detail. The intermediate steps are thoroughly explained so that a beginner can easily follow the material. The book shows a step-by-step implementation of navigation algorithms and provides all the necessary details. It provides detailed illustrations for an easy comprehension. The book also demonstrates real field experiments and in-vehicle road test results with professional discussions and analysis. This work is unique in discussing the different INS/GPS integration schemes in an easy to understand and straightforward way. Those schemes include loosely vs tightly coupled, open loop vs closed loop, and many more.
Author: S. Ebcin
Author: S. Ebcin
Author: Michael G. Giebner
Author: Sedat Ebcin
Author: Nilesh Sharma Gopaul
Author: Jaydev P. Desai
Author: Emilio Frazzoli
Author: Antonio Bicchi
Author: Michael J. Veth
Author: Aboelmagd Noureldin
Author: Paul William McBurney