A Measurement Model for Mobile Robot Localization using Underwater Acoustic Images

Authors Antoni Burguera Burguera | Gabriel Oliver Codina | Yolanda González Cid
In IEEE International Conference on Emerging Technologies and Factory Automation (ETFA), 2010.


Likelihood fields (LF) have been used in the past to perform localization. These approaches infer the LF from range data. However, an underwater Mechanically Scanned Imaging Sonar (MSIS) does not provide distances to the closest obstacles but echo intensity profiles. In this case, obtaining ranges involves processing the acoustic data.
The proposal in this paper avoids the range extraction to build the LF. Instead of processing the acoustic images to obtain ranges and then using these ranges to infer a LF, this paper proposes the use of the acoustic image itself as a good approximation of the LF. The experimental results show the potential benefits of using this idea to define a measurement model to perform mobile robot localization.

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