Grid-based approach for the segmentation of multiple rooms from unstructured indoor point clouds

We present an approach for segmenting an indoor unstructured point cloud into multiple rooms without additional information. Our proposed approach starts by applying a cloth simulation filter (CSF) to the raw dataset to detect point cloud-related ceiling patches without inverting the point cloud. Next, a grid map analysis is conducted for initial room segmentation. It is updated using a morphological erosion process and a neighborhood filter with an adaptive threshold. Finally, boundary recovery is utilized to correct for any incomplete room boundaries obtained from the previous steps. The capabilities and accuracy of our approach were evaluated on different point cloud datasets, and the average recall and precision were 97.13% and 96.60%, respectively. Further validation with the datasets of different levels of noise and registration errors show that this approach achieves an average recall and precision of 99.24% and 99.90%, respectively.