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Robust Particle Filter Based on Huber Function for Underwater Terrain Aided Navigation
KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Robotics, Perception and Learning, RPL. (RPL/EECS)ORCID iD: 0000-0002-7796-1438
2019 (English)In: IET radar, sonar & navigation, ISSN 1751-8784, E-ISSN 1751-8792Article in journal (Refereed) Published
Abstract [en]

Terrain aided navigation is a promising technique to determine the location of underwater vehicle by matching terrain measurement against a known map. The particle filter is a natural choice for terrain aided navigation because of its ability to handle nonlinear, multimodal problems. However, the terrain measurements are vulnerable to outliers, which will cause the particle filter to degrade or even diverge. Modification of the Gaussian likelihood function by using robust cost functions is a way to reduce the effect of outliers on an estimate. We propose to use the Huber function to modify the measurement model used to set importance weights in a particle filter. We verify our method in simulations of multi-beam sonar in a real underwater digital map. The results demonstrate that the proposed method is more robust to outliers than the standard particle filter.

Place, publisher, year, edition, pages
2019.
Keywords [en]
SLAM, mapping, Sonar, AUV
National Category
Robotics
Research subject
Computer Science; Vehicle and Maritime Engineering
Identifiers
URN: urn:nbn:se:kth:diva-256308DOI: 10.1049/iet-rsn.2019.0123ISI: 000494421200002Scopus ID: 2-s2.0-85074255226OAI: oai:DiVA.org:kth-256308DiVA, id: diva2:1344484
Note

QC 20190821

Available from: 2019-08-21 Created: 2019-08-21 Last updated: 2019-12-04Bibliographically approved

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