On the error modeling of dead reckoned data in a distributed virtual environment

Abstract

In this paper, the authors aim to analyze the error of dead reckoned data generated from received data in a discrete temporal axis in a distributed virtual environment (DVE). That is, compared with the data received in continuous time, data acquired in discrete time has a certain degradation or uncertainty of information. Our way of analysis is to introduce a mathematical model of this degradation with regard to the metrics of the temporal interval. We introduced polynomial models for dead reckoning method between frames using parametrics calculated from the data over the last several frames. By employing the method of error analysis of a numerical analysis to the above polynomial models, we formulate theoretical models which approximate the statistical error of dead reckoned data based on parameters such as the update interval and changes in the data. This study enables the discussion on the optimality of the update interval in a DVE using dead reckoning. Finally, we evaluate the adaptability of the theoretical model by conducting simulation experiments generated by pen motion of writing string of letters by human. As a result, we confirm that the proposed theoretical model closely approximates the average error in the simulation.

Quality of Service Impact on Edge Physics Simulations for VR

“…Generally, our system is hindered more than helped by prediction. Previous works have shown the benefits of higher-order predictors can be outweighed by their noise [12], but we show this occurs even for low-order predictors when subject to highly non-linear conditions in real systems: namely, stochastic update-rates and user input. The timescales over which simple predictors work are those which the user is unlikely to notice anything, so long as a high message rate is maintained.…”

“…Further, the return of increasing complexity is limited. For example, Singhal & Cheriton [49] and Lau & Lee [25] demonstrate sensitivity to unmodelled terms, however Hanawa & Yonekura [12] showed better results with lower polynomial-order models. Meng et al [31] noted the context sensitivity of this and proposed a hybrid system that would switch order dynamically.…”

Consensus Based Networking of Distributed Virtual Environments

“…They extrapolated motion based on previous time-stamped samples, including a second-order predictor to support curved motion and a convergence algorithm to reduce spatial jitter. Hanawa & Yonekura [30] analysed zero, one, and second order interpolation polynomials in Lagrange form 1 . Hanawa & Yonekura [31] followed up with a Taylor expansion of 1.…”

Optimizing update scheduling parameters for distributed virtual environments supporting operational test

“…Moreover, the position of the entity on the placed path after Δ t seconds can be approximated using Taylor series expansion, 33 Letting , , and yields the familiar kinematic equation where s ( t ), v ( t ), and a ( t ) represent the entity's position, velocity, and acceleration at time t .…”