Results 261 to 270 of about 86,785 (302)
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1997
In this and the four chapters which follow, we develop the random set approach to data fusion summarized in Section 2.5 of Chapter 2. This chapter sets the stage by developing the mathematical cornerstone of this approach: the concept of a finite random set.
I. R. Goodman +2 more
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In this and the four chapters which follow, we develop the random set approach to data fusion summarized in Section 2.5 of Chapter 2. This chapter sets the stage by developing the mathematical cornerstone of this approach: the concept of a finite random set.
I. R. Goodman +2 more
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Labeled Random Finite Sets With Moment Approximation
IEEE Transactions on Signal Processing, 2017The probability hypothesis density (PHD) filter was proposed as a practical approximation of the multitarget Bayes filter. The cardinalized PHD (CPHD) filter improves on the PHD filter by propagating cardinality distribution. However, both the PHD and CPHD filters have limitations in dealing with missed detections, extracting target state in their ...
Zhejun Lu, Weidong Hu, Thia Kirubarajan
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A Random-Finite-Set Approach to Bayesian SLAM
IEEE Transactions on Robotics, 2011This paper proposes an integrated Bayesian frame work for feature-based simultaneous localization and map building (SLAM) in the general case of uncertain feature number and data association. By modeling the measurements and feature map as random finite sets (RFSs), a formulation of the feature-based SLAM problem is presented that jointly estimates the
John Mullane +3 more
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2011
We begin the justification for the use of RFSs by re-evaluating the basic issues of feature representation, and considering the fundamental mathematical relationship between environmental feature representations, and robot motion. We further the justification for the use of RFSs in FBRM and SLAM by considering an issue of fundamental mathematical ...
Mullane, J. +3 more
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We begin the justification for the use of RFSs by re-evaluating the basic issues of feature representation, and considering the fundamental mathematical relationship between environmental feature representations, and robot motion. We further the justification for the use of RFSs in FBRM and SLAM by considering an issue of fundamental mathematical ...
Mullane, J. +3 more
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Estimation with Random Finite Sets
2011The previous chapter provided the motivation to adopt an RFS representation for the map in both FBRM and SLAM problems. The main advantage of the RFS formulation is that the dimensions of the measurement likelihood and the predicted FBRM or SLAM state do not have to be compatible in the application of Bayes theorem, for optimal state estimation.
Mullane, J. +3 more
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Mobile Robotics in a Random Finite Set Framework
2011This paper describes the Random Finite Set approach to Bayesian mobile robotics, which is based on a natural multi-object filtering framework, making it well suited to both single and swarm-based mobile robotic applications. By modeling the measurements and feature map as random finite sets (RFSs), joint estimates the number and location of the objects
John Mullane +3 more
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The Greatest of a Finite Set of Random Variables
Operations Research, 1961The variables ξ1, …, ξn have a joint normal distribution. We are concerned with the calculation or approximation of max(ξ1, …, ξn). Current analyses and tables handle the case in which the ξı are independently distributed with common expected values and common variances.
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Explicit filtering equations for labelled random finite sets
2015 International Conference on Control, Automation and Information Sciences (ICCAIS), 2015We decompose a probability density function (PDF) of a labelled random finite set (RFS) into a probability mass function over a set of labels and a PDF on a vector-valued multitarget state given the labels. Using this decomposition, we write the Bayesian filtering recursion for labelled RFSs in an explicit form. The resulting formulas are of conceptual
Ángel F. García-Fernández +1 more
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Global robot localization with random finite set statistics
2010 13th International Conference on Information Fusion, 2010We re-examine the problem of global localization of a robot using a rigorous Bayesian framework based on the idea of random finite sets. Random sets allow us to naturally develop a complete model of the underlying problem accounting for the statistics of missed detections and of spurious/erroneously detected (potentially unmodeled) features along with ...
Adrian N. Bishop, Patric Jensfelt
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A random finite set approach to multiple lane detection
2012 15th International IEEE Conference on Intelligent Transportation Systems, 2012Robust lane detection is the precondition for advanced driver assistance systems like lane departure warning and overtaking assistants. While detecting the vehicle's lane is sufficient for lane departure warning, overtaking assistants or autonomous driving functions also need to detect adjacent lanes.
Hendrik Deusch +5 more
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