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A dense structure model for image based stereo SLAM

In this paper a dense structure model is developed for stereo image based Simultaneous Localization And Mapping (SLAM). It is proposed to model dense environment structure incrementally by robustly integrating disparity maps from current and previous time instants. In this way disparities can be refined over time to favor consistent 3D structure over noise. The analytical search bounds for disparities are transferred into the current map to allow efficient re-localization. The cost function is image-based and it is minimized by combining Iteratively Reweighted Least-Squares (IRLS) with exhaustive search for finding motion and disparity parameters respectively