Towards Automated Floorplan Generation

Kunal Chelani, Chitturi Sidhartha, Venu Madhav Govindu

Abstract: In this paper, we propose a pipeline for generating a 2D floorplan using depth cameras. In our pipeline we use an existing approach to recovering the camera motion trajectories from the depth and RGB sequences. Given these motion estimates we construct a full 3D representation of the scanned indoor spaces. For generating a floorplan we need to abstract the large volumes of registered 3D data into a simplified rectilinear representation. We evaluate two approaches to solve this problem, viz slicing the reconstructed volume at a given height and direct segmentation of the 3D point cloud representation into individual planar segments. We also note that the fidelity of our estimated floorplan crucially depends on the accuracy of the estimation of the ground plane orientation. We examine the comparative accuracies of two ground plane estimation methods for each of the above mentioned approaches to rectilinear abstraction. Given the line drawing abstractions of the individual rooms, we merge them into a consistent floorplan. We present results on a real-world floorplan estimation problem and demonstrate its accuracy. Additionally, the implications of errors in the individual components of our pipeline are also studied.