NDLI: Survey on suitable 3D features for sonar-based underwater navigation

Content Provider	IEEE Xplore Digital Library
Author	Woock, P.
Copyright Year	2012
Description	Author affiliation: Fraunhofer Institute of Optronics, System Technologies and Image Exploitation IOSB, Fraunhoferstr. 1 76131 Karlsruhe, Germany (Woock, P.)
Abstract	Navigation of a deep-sea autonomous underwater vehicle (AUV) is a difficult task in cases where no absolute positioning information (e.g., by long baseline systems) is available. Using inertial measurement sensors alone leads to accumulation of sensor noise and with it to corruption of position and orientation estimates over time. Simultaneous localization and mapping (SLAM) techniques can aid to limit the navigation error. In this paper it is discussed what type of features is suitable for sonar-based underwater navigation. For evaluation, a simulation environment has been created which performs environment shape reconstruction based on side-scan sonar data while additionally utilizing vehicular inertial measurement data (ego-motion). A ray-tracing based forward model creates a synthetic sonar response for a given elevation map and vehicle pose. Then, a pose-aware inversion method estimates an elevation map of the surrounding environment. This map is the basis for navigation. Therefore, the characteristics of the reconstruction method need to be taken into account for the feature design as well as the fact that the surface will be sampled differently upon a re-visit.
Starting Page	1
Ending Page	6
File Size	1786644
Page Count	6
File Format	PDF
ISBN	9781457720895
e-ISBN	9781457720918
DOI	10.1109/OCEANS-Yeosu.2012.6263449
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-05-21
Publisher Place	Korea (South)
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Simultaneous localization and mapping Sonar measurements Sonar navigation Vehicles Image reconstruction Robustness
Content Type	Text
Resource Type	Article

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