Single cluster PHD SLAM: Application to autonomous underwater vehicles using stereo vision

Sharad Nagappa, Narcis Palomeras, Chee Sing Lee, Nuno Gracias, Daniel E Clark, Joaquim Salvi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This paper considers the application of feature-based simultaneous localisation and mapping (SLAM) using a random finite sets (RFS) framework for an autonomous underwater vehicle. SLAM allows for reduction in localisation error by tracking features which provide a fixed external reference. The SLAM problem is addressed here using a single-cluster probability hypothesis density (PHD) filter. The filter uses a particle approximation for the vehicle position with a conditional Gaussian mixture PHD for the feature map. Map features are selected as unique point features generated from a stereo camera on-board the vehicle. We demonstrate the improvement in localisation applying the algorithm to a dataset obtained in an indoor test tank.

Original languageEnglish
Title of host publicationMTS/IEEE OCEANS - Bergen
Subtitle of host publicationThe Challenges of the Northern Dimension
PublisherIEEE
ISBN (Print)9781479900022
DOIs
Publication statusPublished - 2013
EventOCEANS 2013 - Bergen, Norway
Duration: 10 Jun 201313 Jun 2013

Conference

ConferenceOCEANS 2013
CountryNorway
CityBergen
Period10/06/1313/06/13

ASJC Scopus subject areas

  • Ocean Engineering

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  • Cite this

    Nagappa, S., Palomeras, N., Lee, C. S., Gracias, N., Clark, D. E., & Salvi, J. (2013). Single cluster PHD SLAM: Application to autonomous underwater vehicles using stereo vision. In MTS/IEEE OCEANS - Bergen: The Challenges of the Northern Dimension [6608107] IEEE. https://doi.org/10.1109/OCEANS-Bergen.2013.6608107