Different underwater vehicles have been developed in order to explore underwater regions, specially those of difficult access for humans. Autonomous Underwater Vehicles (AUVs) are equipped with on-board sensors, which provide valuable information about the vehicle state and the environment. This information is used to build an approximate map of the area and estimate the position of the vehicle within this map. This is the so called Simultaneous Localization and Mapping (SLAM) problem. In this paper we propose a feature based submapping SLAM approach which uses side-scan salient objects as landmarks for the map building process. The detection of salient features in this environment is a complex task, since sonar images are noisy. We present in this paper an algorithm based on a set of image preprocessing steps and the use of a boosted cascade of Haar-like features to perform the automatic detection in side-scan images. Our experimental results show that the method produces consistent maps, while the vehicle is precisely localized. ©2010 IEEE.
|Title of host publication||MTS/IEEE Seattle, OCEANS 2010|
|Publication status||Published - 2010|
|Event||MTS/IEEE Seattle, OCEANS 2010 - Seattle, WA, United States|
Duration: 20 Sep 2010 → 23 Sep 2010
|Conference||MTS/IEEE Seattle, OCEANS 2010|
|Period||20/09/10 → 23/09/10|