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SLAM with SC-PHD filters: an underwater vehicle application

The random finite-set formulation for multiobject estimation provides a means of estimating the number of objects in cluttered environments with missed detections within a unified probabilistic framework. This methodology is now becoming the dominant mathematical framework within the sensor fusion community for developing multiple-target tracking algorithms. These techniques are also gaining traction in the field of feature-based simultaneous localization and mapping (SLAM) for mobile robotics. Here, we present one such instance of this approach with an underwater vehicle using a hierarchical multiobject estimation method for estimating both landmarks and vehicle position

info:eu-repo/grantAgreement/EC/FP7/288273/EU/Persistent Autonomy through Learning, Adaptation, Observation and Re-planning/PANDORA

Institute of Electrical and Electronics Engineers (IEEE)

Author: Lee, Chee Sing
Nagappa, Sharad
Palomeras Rovira, Narcís
Clark, Daniel E.
Salvi, Joaquim
Date: 2014
Abstract: The random finite-set formulation for multiobject estimation provides a means of estimating the number of objects in cluttered environments with missed detections within a unified probabilistic framework. This methodology is now becoming the dominant mathematical framework within the sensor fusion community for developing multiple-target tracking algorithms. These techniques are also gaining traction in the field of feature-based simultaneous localization and mapping (SLAM) for mobile robotics. Here, we present one such instance of this approach with an underwater vehicle using a hierarchical multiobject estimation method for estimating both landmarks and vehicle position
Format: application/pdf
Document access: http://hdl.handle.net/10256/10211
Language: eng
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Collection: info:eu-repo/semantics/altIdentifier/doi/10.1109/MRA.2014.2310132
info:eu-repo/semantics/altIdentifier/issn/1070-9932
Is part of: info:eu-repo/grantAgreement/EC/FP7/288273/EU/Persistent Autonomy through Learning, Adaptation, Observation and Re-planning/PANDORA
Rights: Tots els drets reservats
Subject: Vehicles submergibles
Submersibles
Algorismes computacionals
Computer algorithms
Robots mòbils
Mobile robots
Title: SLAM with SC-PHD filters: an underwater vehicle application
Type: info:eu-repo/semantics/article
Repository: DUGiDocs

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