This paper proposes a Bayesian set-based estimator applied to the adaptive mapping of objects resting on the seabed, through the design and the implementation of a Gaussian Mixture - Probability Hypothesis Density (GM-PHD) filter. Starting from the noisy measures provided by an Autonomous Underwater Vehicle (AUV) equipped with side-scan sonar, the GM-PHD filter produces an on-line estimation of the map of the explored area. The mapping performance are demonstrated through simulations providing the effectiveness of the filter in localizing and mapping new findings and decreasing the spatial uncertainty of the position of known objects.