Automated processing of sonar video imagery enables valuable capabilities for a wide variety of underwater applications in turbid environments. Some key examples comprise the detection, localization and tracking of distinct scene targets, building feature maps, as well as improving positioning accuracy of unmanned submarines by means of image registration and 3D motion estimation to augment traditional positioning devices. This work offers a novel technique for the registration of 2D forward-look sonar images, by optimization over the sonar 3D motion parameters. It incorporates landmark detection through an adaptive clustering scheme with Gaussian map to represent key features at each frame. Improved performance is demonstrated in experiments with real data, both in terms of computation time and accuracy, relative to the state of the art, where the registration utilizes a simplified 2D image transformation model. Among many potentials, the method can improve precision in AUV navigation and environmental modeling.