Quasicircular binary black hole mergers are described by 15 parameters, of which gravitational wave observations can typically constrain only ∼10 independent combinations to varying degree. In this work, we devise co...
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Quasicircular binary black hole mergers are described by 15 parameters, of which gravitational wave observations can typically constrain only ∼10 independent combinations to varying degree. In this work, we devise coordinates that remove correlations, and disentangle well- and poorly-measured quantities. Additionally, we identify approximate discrete symmetries in the posterior as the primary cause of multimodality, and design a method to tackle this type of multimodality. The resulting posteriors have little structure and can be sampled efficiently and robustly. We provide a python package for parameter estimation, cogwheel, that implements these methods together with other algorithms for accelerating the inference process. One of the coordinates we introduce is a spin azimuth that is measured remarkably well in several events. We suggest this might be a sensitive indicator of orbital precession, and we anticipate that it will shed light on the occurrence of spin-orbit misalignment in nature.
Quasicircular binary black hole mergers are described by 15 parameters, of which gravitational wave observations can typically constrain only ∼ 10 independent combinations to varying degree. In this work, we devise c...
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Research software has become a central asset in academic research. It optimizes existing and enables new research methods, implements and embeds research knowledge, and constitutes an essential research product in its...
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We present novel algorithms for predictive tracking of user position and orientation based on double exponential smoothing. These algorithms, when compared against Kalman and extended Kalman filter-based predictors wi...
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We present a testbed for comparing predictive tracking algorithms that allows virtual environment system developers to make better choices about which predictors to use in their environments and aids researchers in de...
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We present a testbed for comparing predictive tracking algorithms that allows virtual environment system developers to make better choices about which predictors to use in their environments and aids researchers in de...
ISBN:
(纸本)9781581136869
We present a testbed for comparing predictive tracking algorithms that allows virtual environment system developers to make better choices about which predictors to use in their environments and aids researchers in determining how predictors work across various virtual environment configurations. Our testbed saves the virtual environment developer and researcher both time and effort with the important task of reducing dynamic tracking error and masking latency. The testbed consists of three components: a prediction algorithm library, a motion data repository, and a graphical testing application which provides users with the ability to test different predictive tracking algorithms across a variety of user motion sequences. The testbed provides enough generality for testing across different algorithmic and system parameters such as sampling rate, prediction time, and noise variance. The paper describes the contents of the predictor library and how to extend it, the types of motion data sets collected thus far, the motion data preparation methodology, and the graphical testing application's functionality and architecture. A simple testing scenario showing output from the testbed is also presented.
We present novel algorithms for predictive tracking of user position and orientation based on double exponential smoothing. These algorithms, when compared against Kalman and extended Kalman filter-based predictors wi...
ISBN:
(纸本)9781581136869
We present novel algorithms for predictive tracking of user position and orientation based on double exponential smoothing. These algorithms, when compared against Kalman and extended Kalman filter-based predictors with derivative free measurement models, run approximately 135 times faster with equivalent prediction performance and simpler implementations. This paper describes these algorithms in detail along with the Kalman and extended Kalman Filter predictors tested against. In addition, we describe the details of a predictor experiment and present empirical results supporting the validity of our claims that these predictors are faster, easier to implement, and perform equivalently to the Kalman and extended Kalman filtering predictors.
We present an experiment comparing double exponential smoothing and Kalman filter-based predictive tracking algorithms with derivative free measurement models. Our results show that the double exponential smoothers ru...
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We present an experiment comparing double exponential smoothing and Kalman filter-based predictive tracking algorithms with derivative free measurement models. Our results show that the double exponential smoothers run approximately 135 times faster with equivalent prediction performance. The paper briefly describes the algorithms used in the experiment and discusses the results.
The unscented Kalman filter is a superior alternative to the extended Kalman filter for a variety of estimation and control problems. However, its effectiveness for improving human motion tracking for virtual reality ...
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The unscented Kalman filter is a superior alternative to the extended Kalman filter for a variety of estimation and control problems. However, its effectiveness for improving human motion tracking for virtual reality applications in the presence of noisy data has been unexplored. In this paper, we present an empirical study comparing the performance of unscented and extended Kalman filtering for improving human head and hand tracking. Specifically, we examine human head and hand orientation motion signals, represented with quaternions, which are critical for correct viewing perspectives in virtual reality. Our experimental results and analysis indicate that unscented Kalman filtering performs equivalently with extended Kalman filtering. However, the additional computational overhead of the unscented Kalman filter and quasi-linear nature of the quaternion dynamics lead to the conclusion that the extended Kalman filter is a better choice for estimating quaternion motion in virtual reality applications.
Presents a novel class of virtual reality input devices that combine pop-through buttons with 6-DOF trackers. Compared to similar devices that use conventional buttons, pop-through devices double the number of potenti...
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ISBN:
(纸本)0769514928
Presents a novel class of virtual reality input devices that combine pop-through buttons with 6-DOF trackers. Compared to similar devices that use conventional buttons, pop-through devices double the number of potential discrete interaction modes, since each button has two activation states corresponding to light and firm pressure. This additional state per button provides a foundation to address a range of shortcomings with conventional virtual environment (VE) input devices that includes reducing the physical dexterity required to perform interactions, reducing the cognitive complexity of some compound tasks and enabling the design of less obtrusive devices without sacrificing expressive power. Specifically, we present two novel input devices: the FingerSleeve was designed to be minimally obtrusive physically, whereas the TriggerGun was designed to be physically similar to, yet more functional than a conventional hand-held trigger device. Further, we present a set of novel navigation and interaction techniques that leverage the capabilities of our pop-through button devices to improve interaction quality and provide insight into harnessing the potential of pop-through buttons for other tasks. Finally, we discuss how we incorporated one of our devices into a real application.
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