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dc.contributor.authorMullane, J.
dc.contributor.authorVo, Ba-Ngu
dc.contributor.authorAdams, M.
dc.contributor.authorVo, Ba Tuong
dc.contributor.editorYing tan, Yuhui Shi, Yi Chai, Guoyin Wang
dc.identifier.citationMullane, J. and Vo, B. and Adams, M. and Vo, B.T. 2011. Mobile Robotics in a Random Finite Set Framework, in Tan, Y. and Shi, Y. and Chai, Y. and Wang, G. (ed), Advances in Swarm Intelligence: Lecture Notes in Computer Science Part 2. 6729: pp. 519-528. Berlin, Heidelberg: Springer.

This 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 (features) in the map can be generated. In addition, it is shown how the path of each robot can be estimated if required. The framework differs dramatically from existing approaches since both data association and feature management routines are integrated into a single recursion. This makes the framework well suited to multi-robot scenarios due to the ease of fusing multiple map estimates from swarm members, as well as mapping robustness in the presence of other mobile robots which may induce false map measurements. An overview of developments thus far is presented, with implementations demonstrating the merits of the framework on simulated and experimental datasets.

dc.subjectmobile robotics
dc.subjectBayesian estimation
dc.subjectProbability Hypothesis Density
dc.subjectrandom finite sets
dc.titleMobile Robotics in a Random Finite Set Framework
dc.typeBook Chapter
dcterms.source.titleAdvances in Swarm Intelligence Lecture Notes in Computer Science Volume 6729
dcterms.source.placeBerlin Heidelberg
curtin.accessStatusFulltext not available

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