Journal ArticleDOI
Survey of nonlinear attitude estimation methods
TLDR
A survey of modern nonlinear filtering methods for attitude estimation based on the Gaussian assumption that the probability density function is adequately specified by its mean and covariance is provided.Abstract:
This paper provides a survey of modern nonlinear filtering methods for attitude estimation. Early applications relied mostly on the extended Kalman filter for attitude estimation. Since these applications, several new approaches have been developed that have proven to be superior to the extended Kalman filter. Several of these approaches maintain the basic structure of the extended Kalman filter, but employ various modifications in order to provide better convergence or improve other performance characteristics. Examples of such approaches include: filter QUEST, extended QUEST and the backwards-smoothing extended Kalman filter. Filters that propagate and update a discrete set of sigma points rather than using linearized equations for the mean and covariance are also reviewed. A twostep approach is discussed with a first-step state that linearizes the measurement model and an iterative second step to recover the desired attitude states. These approaches are all based on the Gaussian assumption that the probability density function is adequately specified by its mean and covariance. Other approaches that do not require this assumption are reviewed, Associate Professor, Department of Mechanical & Aerospace Engineering. Email: johnc@eng.buffalo.edu. Associate Fellow AIAA. Aerospace Engineer, Guidance, Navigation and Control Systems Engineering Branch. Email: Landis.Markley@nasa.gov. Fellow AIAA. Postdoctoral Research Fellow, Department of Mechanical & Aerospace Engineering. Email: cheng3@eng.buffalo.edu. Member AIAA.read more
Citations
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An optimization-based approach to human body motion capture using inertial sensors
TL;DR: In this article, an optimization-based solution to magnetometer-free inertial motion capture is presented, which allows for natural inclusion of biomechanical constraints, for handling of nonlinearities and for using all data in obtaining an estimate.
Journal ArticleDOI
Implementation of a Nonlinear Attitude Estimator for Aerial Robotic Vehicles
TL;DR: The theoretical and practical solutions in order to obtain a robust nonlinear attitude estimator for flying vehicles equipped with low-cost sensors and the fixed-point numerical implementation of the algorithm are detailed.
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Magnetometer Calibration Using Inertial Sensors
Manon Kok,Thomas B. Schön +1 more
TL;DR: A practical algorithm for calibrating a magnetometer for the presence of magnetic disturbances and for magnetometer sensor errors is presented and is shown to give good results using data from two different commercially available sensor units.
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Gradient-like observers for invariant dynamics on a Lie group
TL;DR: A design methodology for the innovation term based on gradient-like terms derived from invariant or non-invariant cost functions is proposed and the resulting nonlinear observers have strong (almost) global convergence properties.
Proceedings ArticleDOI
Point Pair Features Based Object Detection and Pose Estimation Revisited
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TL;DR: A revised pipe-line of the existing 3D object detection and pose estimation framework based on point pair feature matching is presented, and it is argued that such a combined pipeline simultaneously boosts the detection rate and reduces the complexity, while improving the accuracy of the resulting pose.
References
More filters
Journal ArticleDOI
A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking
TL;DR: Both optimal and suboptimal Bayesian algorithms for nonlinear/non-Gaussian tracking problems, with a focus on particle filters are reviewed.