Semantic world modeling using probabilistic multiple hypothesis anchoring

In order to successfully perform typical household tasks such as manipulation or navigation, domestic robots need an accurate description of the world they are operating in. Creating and maintaining such a description, in this work referred to as world model, is a non-trivial task in a domestic envi...

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Vydáno v:Robotics and autonomous systems Ročník 61; číslo 2; s. 95 - 105
Hlavní autoři: Elfring, J., van den Dries, S., van de Molengraft, M.J.G., Steinbuch, M.
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier B.V 01.02.2013
Témata:
Anchoring
Data association
Domestic
Mathematical models
Multiple model tracking
Probabilistic methods
Probabilistic multiple hypothesis anchoring
Probability theory
Robots
Tasks
Tracking
World modeling
World modeling
Probabilistic multiple hypothesis anchoring
Anchoring
Multiple model tracking
Data association
ISSN:0921-8890, 1872-793X
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Shrnutí:In order to successfully perform typical household tasks such as manipulation or navigation, domestic robots need an accurate description of the world they are operating in. Creating and maintaining such a description, in this work referred to as world model, is a non-trivial task in a domestic environment that typically has a high number of objects, and is unstructured and dynamically changing. This work introduces probabilistic multiple hypothesis anchoring to create and maintain a semantically rich world model using probabilistic anchoring. Multiple hypothesis tracking-based data association is included to be able to deal with ambiguous scenarios. Multiple model tracking is included to be able to easily incorporate different kinds of prior knowledge. ► We introduce probabilistic multiple hypothesis anchoring. ► Anchoring with multiple model tracking and multiple hypothesis based data association. ► Experimental validation of the approach in a series of challenging experiments. ► An analysis of the (im)possibilities of the method based on the assumptions made.
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ISSN:0921-8890
1872-793X
DOI:10.1016/j.robot.2012.11.005