38 research outputs found
Nonlinear Attitude Filtering: A Comparison Study
This paper contains a concise comparison of a number of nonlinear attitude
filtering methods that have attracted attention in the robotics and aviation
literature. With the help of previously published surveys and comparison
studies, the vast literature on the subject is narrowed down to a small pool of
competitive attitude filters. Amongst these filters is a second-order optimal
minimum-energy filter recently proposed by the authors. Easily comparable
discretized unit quaternion implementations of the selected filters are
provided. We conduct a simulation study and compare the transient behaviour and
asymptotic convergence of these filters in two scenarios with different
initialization and measurement errors inspired by applications in unmanned
aerial robotics and space flight. The second-order optimal minimum-energy
filter is shown to have the best performance of all filters, including the
industry standard multiplicative extended Kalman filter (MEKF)
Specific phobia predicts psychopathology in young women
Contains fulltext :
90255.pdf (publisher's version ) (Closed access)Although specific phobia is characterized by an early age at onset and by high rates of comorbidity, few studies have examined comorbid relationships prospectively.
The present study investigated the association between specific phobia and the risk of a broad range of psychopathology among young women in the community.
Data came from the Dresden Predictor Study in which 1,538 German women (18-25 years) completed a diagnostic interview at two time points.
Women with specific phobia had a twofold increase in odds of developing any anxiety disorder, generalized anxiety disorder, depression, and any somatoform disorder during 17 months, compared to women without specific phobia. Except for depression, these associations persisted after adjustment for all comorbid mental disorders.
Specific phobia thus appears to be a risk factor for a variety of problems. The result further underpins the necessity for early intervention for specific phobia to prevent later mental health problems
A Generalized Projective Reconstruction Theorem and Depth Constraints for Projective Factorization
This paper presents a generalized version of the classic projective reconstruction theorem which helps to choose or assess depth constraints for projective depth estimation algorithms. The theorem shows that projective reconstruction is possible under a much weaker constraint than requiring all estimated projective depths to be nonzero. This result enables us to present classes of depth constraints under which any reconstruction of cameras and points projecting into given image points is projectively equivalent to the true camera-point configuration. It also completely specifies the possible wrong configurations allowed by other constraints. We demonstrate the application of the theorem by analysing several constraints used in the literature, as well as presenting new constraints with desirable properties. We mention some of the implications of our results on iterative depth estimation algorithms and projective reconstruction via rank minimization. Our theory is verified by running experiments on both synthetic and real data
Gradient-like observers on semidirect products
Abstract-This paper proposes a simple full state observer design for invariant state space systems where the state is evolving on a semidirect product of connected, finite-dimensional Lie groups. The design is based on a pair of cost functions defined on the Lie groups and consists of a copy of the observed system and a gradient-like innovation term. Under mild conditions the observer displays almost global exponential convergence. We illustrate the construction by an application in pose estimation