An efficient approach to bathymetric SLAM

Authors:
Stephen Barkby;Stefan Williams;Oscar Pizarro;Michael Jakuba
Affiliations:
ARC Center of Excellence for Autonomous Systems, School of Aerospace Mechanical and Mechatronic Engineering, University of Sydney, Sydney, NSW, Australia;ARC Center of Excellence for Autonomous Systems, School of Aerospace Mechanical and Mechatronic Engineering, University of Sydney, Sydney, NSW, Australia;ARC Center of Excellence for Autonomous Systems, School of Aerospace Mechanical and Mechatronic Engineering, University of Sydney, Sydney, NSW, Australia;ARC Center of Excellence for Autonomous Systems, School of Aerospace Mechanical and Mechatronic Engineering, University of Sydney, Sydney, NSW, Australia
Venue:
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Year:
2009

Citing 3
Cited 2

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Abstract

In this paper we propose an approach to SLAM suitable for bathymetric mapping by an Autonomous Underwater Vehicle (AUV). AUVs typically do not have access to GPS while underway and the survey areas of interest are unlikely to contain features that can easily be identified and tracked using bathymetric sonar. We demonstrate how the uncertainty in the vehicle state can be modeled using a particle filter and an Extended Kalman Filter (EKF), where each particle maintains a 2D depth map to model the seafloor. Efficient methods for maintaining and resampling the joint maps and particles using Distributed Particle Mapping are then described. Our algorithm was tested using field data collected by an AUV equipped with multibeam sonar. The results achieved by Bathymetric distributed Particle SLAM (BPSLAM) demonstrate how observations of the seafloor structure improve the estimated trajectory and resulting map when compared to dead reckoning fused with USBL observations, the best navigation solution during the trials. Furthermore, the computational run time to deliver these results falls well below the total mission time, providing the potential for the algorithm to be implemented in real time.