The 7th IFAC Symposium on Intelligent Autonomous Vehicles 2010

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Comparison between feature-based and phase correlation methods for ROV vision-based speed estimation

Fausto Ferreira
CNR-IEIIT
Italy

Francesco Orsenigo
HCMR
Greece

Gianmarco Veruggio
CNR-IEIIT
Italy

Petros Pavlakis
HCMR
Greece

Massimo Caccia
CNR-ISSIA
Italy

Gabriele Bruzzone

Abstract:
The performance of different visual approaches for estimating the
motion of an underwater Remotely Operated Vehicle (ROV) is
discussed.

The paper compares three different techniques: feature
correlation, Speeded Up Robust Features (SURF), both based on
feature extraction and matching, and phase correlation, which
instead does not rely on image features.

The three algorithms accuracy and performance are
compared using a batch of data collected in
typical operating conditions with the Romeo ROV.

In estimating vehicle speed, phase correlation outperformed
SURF in terms of robustness and precision, giving similar results
to those obtained with feature correlation.
In terms of computational time, phase correlation outperformed both
feature-based methods.

 

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