Tracking the spin on a ping pong ball with the quaternion Bingham filter

Glover, Jared; Kaelbling, Leslie Pack

doi:10.1109/icra.2014.6907460

Cited by 52 publications

(37 citation statements)

References 16 publications

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“…In recent years, a number of approaches have been proposed to estimate the orientation using these kinds of distributions. For instance, in [77,44,45] algorithms are presented to estimate orientation by modeling it using a Bingham distribution.…”

Section: Alternative Methodsmentioning

confidence: 99%

Using Inertial Sensors for Position and Orientation Estimation

Kok

Hol

Schön

2017

FNT in Signal Processing

300

287

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In recent years, microelectromechanical system (MEMS) inertial sensors (3D accelerometers and 3D gyroscopes) have become widely available due to their small size and low cost. Inertial sensor measurements are obtained at high sampling rates and can be integrated to obtain position and orientation information. These estimates are accurate on a short time scale, but suffer from integration drift over longer time scales. To overcome this issue, inertial sensors are typically combined with additional sensors and models. In this tutorial we focus on the signal processing aspects of position and orientation estimation using inertial sensors. We discuss different modeling choices and a selected number of important algorithms. The algorithms include optimization-based smoothing and filtering as well as computationally cheaper extended Kalman filter and complementary filter implementations. The quality of their estimates is illustrated using both experimental and simulated data.

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Section: Alternative Methodsmentioning

confidence: 99%

Using Inertial Sensors for Position and Orientation Estimation

Kok

Hol

Schön

2017

FNT in Signal Processing

300

287

View full text Add to dashboard Cite

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“…As future work, this issue could be addressed by studying alternative ways of modeling quaternion distributions that consider the fact that quaternions are elements of S 3 . One possible avenue could be that of exploiting the Bingham distribution [17].…”

Section: Discussionmentioning

confidence: 99%

Learning bimanual end-effector poses from demonstrations using task-parameterized dynamical systems

Silvério

Rozo

Calinon

2015

2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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Very often, when addressing the problem of human-robot skill transfer in task space, only the Cartesian position of the end-effector is encoded by the learning algorithms, instead of the full pose. However, orientation is just as important as position, if not more, when it comes to successfully performing a manipulation task. In this paper, we present a framework that allows robots to learn the full poses of their end-effectors in a task-parameterized manner. Our approach permits the encoding of complex skills, such as those found in bimanual manipulation scenarios, where the generalized coordination patterns between end-effectors (i.e. position and orientation patterns) need to be considered. The proposed framework combines a dynamical systems formulation of the demonstrated trajectories, both in R 3 and SO(3), and taskparameterized probabilistic models that build local task representations in both spaces, based on which it is possible to extract the relevant features of the demonstrated skill. We validate our approach with an experiment in which two 7-DoF WAM robots learn to perform a bimanual sweeping task.

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“…The name refers to the estimation of an axis, e.g., a rotation axis. We provide the Bingham filter described in Kurz et al (2014f), a special case of which was also considered by Glover and Kaelbling (2013). Furthermore, the Bingham filter contains the unscented extension proposed in Gilitschenski et al (2016b).…”

Section: Hyperspherementioning

confidence: 99%

“…Directional statistics is a subfield of statistics that deals with quantities defined on manifolds such as the unit circle or the unit hypersphere. Originally mostly developed with geoscientific applications in mind (Mardia 1981;Bingham 1974;Gaile and Burt 1980), directional statistics has gained widespread interest in various areas during the past decades, for example in biology (Batschelet 1981;Mardia, Taylor, and Subramaniam 2007), robotics (Glover and Kaelbling 2014;Feiten, Lang, and Hirche 2013;Markovic, Chaumette, and Petrovic 2014), machine learning (Banerjee, Dhillon, Ghosh, and Sra 2005;Gopal and Yang 2014;Diethe, Twomey, and Flach 2015), aerospace (Horwood and Poore 2014;Darling and DeMars 2015;, and signal processing (Traa and Smaragdis 2013;Azmani, Reboul, Choquel, and Benjelloun 2009;Drude, Chinaev, Vu, and Haeb-Umbach 2014). A good introduction to the topic can, for example, be found in the book by Mardia and Jupp (1999).…”

Section: Introductionmentioning

confidence: 99%

Directional Statistics and Filtering Using libDirectional

Kurz¹,

Gilitschenski²,

Pfaff³

et al. 2019

J. Stat. Soft.

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In this paper, we present libDirectional, a MATLAB library for directional statistics and directional estimation. It supports a variety of commonly used distributions on the unit circle, such as the von Mises, wrapped normal, and wrapped Cauchy distributions. Furthermore, various distributions on higher-dimensional manifolds such as the unit hypersphere and the hypertorus are available. Based on these distributions, several recursive filtering algorithms in libDirectional allow estimation on these manifolds. The functionality is implemented in a clear, well-documented, and object-oriented structure that is both easy to use and easy to extend.

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Tracking the spin on a ping pong ball with the quaternion Bingham filter

Abstract: Abstract-

Cited by 52 publications

References 16 publications

Using Inertial Sensors for Position and Orientation Estimation

Using Inertial Sensors for Position and Orientation Estimation

Learning bimanual end-effector poses from demonstrations using task-parameterized dynamical systems

Directional Statistics and Filtering Using libDirectional

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