A Novel Framework for Structure Descriptors-Guided Hand-drawn Floor Plan Reconstruction

In the absence of a pre-built indoor map, robot navigation suffers from the limitations of sensors and environments, resulting in decreased efficiency in performing ad-hoc tasks. Given that blueprints are difficult to obtain, an intuitive method is to provide robots with prior knowledge via hand-drawn floor plans. However, due to the inability of robots to directly comprehend hand-drawn styles, the applicability of this method is limited. In this paper, we present a novel framework for hand-drawn floor plan reconstruction that can recognize abstract hand-drawn elements and standardize the reconstruction of hand-drawn floor plans, thereby providing robots with valuable global map information. Specifically, we design a new series of structure descriptors as reconstruction components and employ a deep learning-based model for recognition. Then the standardized results are obtained through the proposed floor plan reconstruction algorithm. To verify the effectiveness of the framework, we conduct experiments on electronic and paper hand-drawn floor plans. Compared with other state-of-the-art methods, our proposed method achieves superior reconstruction results. This work expands the application scenarios for indoor robots, enabling them to quickly comprehend the semantics of complex scenes, thereby enhancing the competitiveness in downstream tasks.