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Inertial navigation system

About: Inertial navigation system is a research topic. Over the lifetime, 14582 publications have been published within this topic receiving 190618 citations. The topic is also known as: intertial guidance system & inertial reference platform.


Papers
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Journal ArticleDOI
TL;DR: An integrated approach to ‘loop-closure’, that is the recognition of previously seen locations and the topological re-adjustment of the traveled path, is described, where loop-closure can be performed without the need to re-compute past trajectories or perform bundle adjustment.
Abstract: We describe a model to estimate motion from monocular visual and inertial measurements. We analyze the model and characterize the conditions under which its state is observable, and its parameters are identifiable. These include the unknown gravity vector, and the unknown transformation between the camera coordinate frame and the inertial unit. We show that it is possible to estimate both state and parameters as part of an on-line procedure, but only provided that the motion sequence is â??rich enoughâ??, a condition that we characterize explicitly. We then describe an efficient implementation of a filter to estimate the state and parameters of this model, including gravity and camera-to-inertial calibration. It runs in real-time on an embedded platform. We report experiments of continuous operation, without failures, re-initialization, or re-calibration, on paths of length up to 30 km. We also describe an integrated approach to â??loop-closureâ??, that is the recognition of previously seen locations and the topological re-adjustment of the traveled path. It represents visual features relative to the global orientation reference provided by the gravity vector estimated by the filter, and relative to the scale provided by their known position within the map; these features are organized into â??locationsâ?? defined by visibility constraints, represented in a topological graph, where loop-closure can be performed without the need to re-compute past trajectories or perform bundle adjustment. The software infrastructure as well as the embedded platform is described in detail in a previous technical report.

512 citations

Book
01 Jan 1971
TL;DR: In this paper, a mechanization and error analysis of inertial navigation systems is presented, with an emphasis on terrestrial applications on or slightly above the earth's surface, and a unified error analysis technique is developed that is applicable to virtually all system configurations.
Abstract: This volume offers the avionic systems engineer a fundamental exposition of the mechanization and error analysis of inertial navigation systems. While the material is applicable to spacecraft and undersea navigation, emphasis is placed upon terrestrial applications on or slightly above the earth's surface. As a result, practical considerations are geared toward those aircraft navigation systems of particular current interest. Extensive use is made of perturbation techniques to develop linearized system equations, whose solutions closely approximate those obtained by nonlinear differential equations. A unified error analysis technique is developed that is applicable to virtually all system configurations. The technique provides a greatly simplified method for comparing the performance of competing system configurations.

483 citations

Book
01 Apr 2013
TL;DR: The second edition of the Artech House book Principles of GNSS, Inertial, and Multisensor Integrated Navigation Systems as discussed by the authors offers a current and comprehensive understanding of satellite navigation, inertial navigation, terrestrial radio navigation, dead reckoning, and environmental feature matching.
Abstract: This newly revised and greatly expanded edition of the popular Artech House book Principles of GNSS, Inertial, and Multisensor Integrated Navigation Systems offers you a current and comprehensive understanding of satellite navigation, inertial navigation, terrestrial radio navigation, dead reckoning, and environmental feature matching . It provides both an introduction to navigation systems and an in-depth treatment of INS/GNSS and multisensor integration. The second edition offers a wealth of added and updated material, including a brand new chapter on the principles of radio positioning and a chapter devoted to important applications in the field. Other updates include expanded treatments of map matching, image-based navigation, attitude determination, acoustic positioning, pedestrian navigation, advanced GNSS techniques, and several terrestrial and short-range radio positioning technologies. The book shows you how satellite, inertial, and other navigation technologies work, and focuses on processing chains and error sources. In addition, you get a clear introduction to coordinate frames, multi-frame kinematics, Earth models, gravity, Kalman filtering, and nonlinear filtering. Providing solutions to common integration problems, the book describes and compares different integration architectures, and explains how to model different error sources. You get a broad and penetrating overview of current technology and are brought up to speed with the latest developments in the field, including context-dependent and cooperative positioning. DVD Included: Features eleven appendices, interactive worked examples, basic GNSS and INS MATLAB simulation software, and problems and exercises to help you master the material.

483 citations

Journal ArticleDOI
01 Oct 2001
TL;DR: The algorithm exploits nonholonomic constraints that govern the motion of a vehicle on a surface to obtain velocity observation measurements which aid in the estimation of the alignment of the IMU as well as the forward velocity of the vehicle.
Abstract: This paper presents a new method for improving the accuracy of inertial measurement units (IMUs) mounted on land vehicles. The algorithm exploits nonholonomic constraints that govern the motion of a vehicle on a surface to obtain velocity observation measurements which aid in the estimation of the alignment of the IMU as well as the forward velocity of the vehicle. It is shown that this can be achieved without any external sensing provided that certain observability conditions are met. A theoretical analysis is provided together with a comparison of experimental results between a nonlinear implementation of the algorithm and an IMU/GPS navigation system. This comparison demonstrates the effectiveness of the algorithm. The real time implementation is also addressed through a multiple observation inertial aiding algorithm based on the information filter. The results show that the use of these constraints and vehicle speed guarantees the observability of the velocity and the attitude of the inertial unit, and hence bounds the errors associated with these states. The strategies proposed provides a tighter navigation loop which can sustain outages of GPS for a greater amount of time as compared to when the inertial unit is used with standard integration algorithms.

470 citations

Book
01 Jan 1997
TL;DR: In this article, a central force gravity field model is used for inertial navigation with assistance from external measurements. But this model is not suitable for the Kalman Filter State Variable Error Models.
Abstract: Part 1 Inertial Navigation: Notation, Coordinate Systems and Units Equations of Motion in a Central Force Gravity Field Inertial Instrumentation Calibration Initial Alignment and Attitude Computation Geodetic Variables and Constants Equations of Motion with General Gravity Model. Part 2 Inertial Navigation with Aids: Inertial Navigation with External Measurements Error Equations for the Kalman Filter State Variable Error Models. Part 3 Accuracy Analysis: Accuracy Criteria and Analysis Techniques Error Equations for Calibration, Alignment and Initialization Evaluation of Gravity Model Error Effects. Appendices: Matrix Inverse Formulas LaPlace Transforms Quaternions Associated Legendre Functions Associated Legendre Function Derivatives Procedure for Generating Gravity Disturbance Realizations Procedure for Generating Specific Force Profile.

470 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
2023309
2022657
2021491
2020889
20191,003
20181,013