Floorplans are useful for navigating indoor spaces, for resource allocation and for indoor space management among many others. But in the absence of readily available digital floorplans, these are hard to generate. In this work, we enhance the generation of floor- plans from point clouds to be more robust to noisy measurements of sensor data. In our approach, we train an object detector to expose room shapes in a density map produced from a 3D point cloud, as well as the position of relevant landmarks, such as doors and windows. We improve the robustness of the room detector by training in two stages, firstly using point clouds extracted from synthetic 3D graphical representations of plausible indoor spaces; and secondly, extending the training of the model in a new do- main by using real-world data collected with Tango devices. This two-tier training nudges the model closer to our target domain, of generating floorplans from easily collected point cloud scans in the real-world. Finally, we showcase the capability of our solution when operating with noisy Lidar scans collected from a drone with pose estimation.
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