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Having defined measured distance, we can now define the framework for localization.
Section 4 presents a non-linear least squares (NLS) framework for localization, where the separable least squares (SLS) principle can be used to eliminate nuisance parameters.
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Then, we describe the statistical framework for cooperative localization using NBP proposed by Ihler et al. [2 4].
For example, Weerasinghe et al. (2012) propose a Kinect-based tracking framework for the localization of workers and the analysis of their movement patterns, which could potentially be used for productivity measurement.
Figure 1: The baysian model for localization.
It is also worth noting that, unlike other methods, the ensLOC framework for quantifying subcellular localization does not restrict a protein to a single localization class.
In this paper, we provide a general Bayesian framework for schedule-based localization which takes into account uncertainty in anchor location and in timing devices.
Our end-to-end framework for spatiotemporal interaction localization aids in the automated analysis of videos.
In this paper we discuss a framework for visual self-localization of mobile robots using a parametric model built from panoramic snapshots of the environment.
(ii) We increase the number of classes covered by a single consistent framework: 3 localization classes for archaea, 6 for bacteria and 18 for eukaryota.
Monte Carlo Localization (MCL) has become a popular framework for solving the self-localization problem in mobile robots.
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