NDLI: Bathymetric SLAM with no map overlap using Gaussian Processes

Content Provider	IEEE Xplore Digital Library
Author	Barkby, S. Williams, S.B. Pizarro, O. Jakuba, M.V.
Copyright Year	2011
Description	Author affiliation: Australian Centre for Field Robotics, School of Aerospace Mechanical and Mechatronic Engineering, University of Sydney, NSW, 2006, Australia (Barkby, S.; Williams, S.B.; Pizarro, O.; Jakuba, M.V.)
Abstract	This paper presents an efficient and featureless approach to Bathymetric Simultaneous Localization And Mapping (SLAM) that utilizes a Rao-Blackwellized Particle Filter (RBPF) and Gaussian Process (GP) Regression to provide loop closures in areas where little to no overlap with previously explored terrain is present. To significantly reduce the memory requirements of this approach (thereby allowing for the processing of large datasets) a novel map representation is also introduced that, instead of directly storing estimates of seabed depth, records the trajectory of each particle and synchronizes them to a common log of bathymetric observations. Upon detecting a loop closure each particle is then weighted by matching new observations to the current predictions generated from a local reconstruction of their map using GP Regression. Here the spatial correlation in the environment is fully exploited, allowing predictions of seabed depth to be generated in areas that may not have been directly observed previously. The particle resampling that is performed therefore not only enforces self-consistency in overlapping sections of the map but additionally enforces self-consistency between neighboring map borders. The results demonstrate how observations of seafloor structure with partial overlap can be used by bathymetric SLAM to improve map self consistency when compared to Dead Reckoning fused with Long-Baseline observations. In addition we show how mapping corrections can still be achieved even when no map overlap is present.
Starting Page	1242
Ending Page	1248
File Size	2129269
Page Count	7
File Format	PDF
ISBN	9781612844541
ISSN	21530858
e-ISBN	9781612844565
DOI	10.1109/IROS.2011.6094730
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-09-25
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Trajectory Navigation Vehicles Simultaneous localization and mapping Uncertainty Atmospheric measurements Particle measurements
Content Type	Text
Resource Type	Article
Subject	Control and Systems Engineering Computer Science Applications Computer Vision and Pattern Recognition Software

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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