This paper presents an approach for sonar feature map building. The approach is composed of extracting features at the data-level fusion stage and fusing the extracted features with the registered features in the map at the feature-level fusion stage. A data-level fusion model, termed three measurements association model (TMAM), has been developed for associating three measurements with a line or a point feature. By use of TMAM, different sets of measurements obtained from a single sonar sensor at consecutive steps are associated with the line and point features. Subsequently, the parameters of the identified features are estimated by use of the iterated least square estimation method. Finally, when a feature is extracted, a simple feature-level fusion strategy is used to update the map. The proposed approach has been tested both in simulation and on real data.

We propose a novel scalable sub-patch algorithm for texture synthesis. Texture synthesis is an important research topic in computer graphics and image processing. We present an efficient algorithm which can paste a patch each time without boundary processing technique. In particular, the proposed sub-patch algorithm can be used in constrained texture synthesis to fill the large lost region. On the other hand, the searching neighborhood pixels can be changed due to different patch size in image reconstruction. The comparison of previous algorithms is also provided in this paper. We present a first algorithm used in constrained texture synthesis by regular synthesizing order.