�bstract—Simultaneous Localization and Mapping �SLAM) consists on building a map of an unknown environment, while simultaneously determining the location of the vehicle within this map. Building a map implies finding a proper representation for its salient features, which are used as landmarks for the localization problem. These landmarks must be very robust in order to be easily detected once reobserved. Associating a new observation with a previously seen landmark provides a proper input for the map and localization update. Instead, wrong associations introduce divergences and inconsistencies in the results. The aim of this paper is to introduce an approach able to detect objects in side-scan sonar images. Side-scan sonar provides high resolution acoustic images, in which an object appears as a bright spot with a dark shadow trail. In order to have a fast and robust object detector, we adapted the framework introduced by Viola and Jones, in which a cascade of classifiers was used to perform a fast face detection with high detection rates. The performance of our detection method is presented, together with the SLAM results obtained after using our robust landmark detector. The results produced high detection rates and small number of false positives, demonstrating the validity of our approach.