Gated-Dual-Attention-Based Full-Resolution Floor Plan Segmentation Network for Topological Semantic Mapping

Abstract

Topological semantic maps serve as an effective tool for the partially sighted or visually impaired (PSVI) in indoor navigation. Essential to their construction is the precise segmentation of floor plans. However, current techniques fall short in segmentation performance. To overcome this, we introduce a gated-dual-attention-based full-resolution network (GFNet) for floor plan segmentation. We leverage the inherent low-stage detailed features and intraclass-and-interclass contextual dependencies within floor plans to maximize segmentation effectiveness. Our proposed network incorporates modified residual blocks in early stages to maintain full-resolution capture with a low parameter count. We design and apply a novel gated dual attention (GDA) module that efficiently integrates channel and spatial contextual information to enhance local feature representation. This module improves the overall performance of our network while ensuring minimal parameter fluctuation. We also propose a 2-D deep supervision (TDDS) method to merge features from all stages, further enhancing the multilevel feature representation ability. Finally, a practical topological semantic mapping method for PSVI indoor navigation is introduced. All models are evaluated on the rasterized floor plan datasets R2V and R3D. Experimental results show that the proposed network achieves 2.21% and 2.74% mIoU improvements over the previous state-of-the-art method on R2V and R3D, respectively, for the challenging categories of walls and doors. This contributes to more accurate topological semantic mapping.