A Variational Bayes approach for reliable underwater navigation

Georgios Fagogenis, David Lane

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

1 Citation (Scopus)

Abstract

This paper presents a filtering algorithm for non-linear systems in the case of sensor degradation. The algorithm adapts the relative importance of the sensor measurements, compared to the model predictions, in real time; yielding a filter that is robust to noisy observations and sensor blackouts. The filter is constructed using a Variational Bayes Approximation of the conditional probability distribution of the system's state; i.e., the probability distribution of the state, given the measurements from the sensors. The algorithm is evaluated both in simulation and experimentally on a robotic platform. In the experiments, the sensor measurements from an Autonomous Underwater Vehicle (AUV) are altered artificially. The sensor output is either corrupted with outliers or manually stuck to a constant value; simulating in this fashion a sensor defect. In both cases, the filter reconstructs the robot's state accurately, thus enabling the vehicle to resume with mission execution.

Original languageEnglish
Title of host publication2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
PublisherIEEE
Pages2252-2257
Number of pages6
ISBN (Print)9781479999941
DOIs
Publication statusPublished - 2015
Event28th IEEE/RSJ International Conference on Intelligent Robots and Systems 2015 - Hamburg, Germany
Duration: 28 Sep 20152 Oct 2015

Conference

Conference28th IEEE/RSJ International Conference on Intelligent Robots and Systems 2015
Abbreviated titleIROS 2015
CountryGermany
CityHamburg
Period28/09/152/10/15

Keywords

  • Approximation algorithms
  • Kalman filters
  • Mathematical model
  • Navigation
  • Prediction algorithms
  • Probability distribution
  • Robot sensing systems

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Software
  • Computer Vision and Pattern Recognition
  • Computer Science Applications

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  • Cite this

    Fagogenis, G., & Lane, D. (2015). A Variational Bayes approach for reliable underwater navigation. In 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (pp. 2252-2257). IEEE. https://doi.org/10.1109/IROS.2015.7353679