From SLAM to CAD Maps and Back Using Generative Models

In Simultaneous Localization and Mapping (SLAM), quality maps play an important role beyond the implicit benefits they have in localization. For example, good quality SLAM maps could be used as ’pre-maps’ for generating higher semantic meaning and geometric structure, such as in 2D CAD drawings in which doors, windows, and other entities carry meaning. Unfortunately, in its raw form, the quality of a SLAM map is limited by several software and hardware related factors. This paper proposes to address this problem by converting SLAM maps to CAD maps based on a generative adversarial network (GAN). The paper also investigates the inverse problem, that of generating SLAM maps from 2D CAD drawings, when synthetic SLAM maps are needed. We do so by proposing a method based on an analytical model and another on GANs. Experiments demonstrate both qualitatively and quantitatively the success of the proposed approaches.