Kinetic expansion of linear structural elements : a hybrid method for floorplan reconstruction from indoor scene point cloud

Abstract

Indoor floorplans are widely used in fields like building information modeling, indoor navigation, emergency response, smart buildings, and architectural design simulation. However, reconstructing accurate floorplans from indoor laser point clouds is challenging due to the complexity, clutter, and occlusions of indoor structures. We propose kinetic expansion of linear structural elements (KELSE), an indoor scene floorplan reconstruction method to address these challenges. We design a structural element extraction method that integrates geometric feature constraints with semantic information to identify structural elements such as walls, doors, windows, ceilings, and floors in complex indoor scenes. A kinetic data structure expansion and undirected graph optimization are then used to reconstruct the complete floorplan. Experimental results show that KELSE achieves high accuracy and completeness, with room reconstruction reaching 0.98 and 0.95, respectively. KELSE provides an efficient and precise solution for floorplan reconstruction from indoor LiDAR point cloud data.