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.
Morphological and triangular irregular network (TIN) ground filters require setting up different parameters to achieve high accuracy for different terrains. A proposed morphologically iterative TIN (MIT) ground filter only requires maximum building size in the processing of raw light detection and ranging (LiDAR) data. This approach applies morphological and TIN densification in an iterative way for separating ground points from off-ground ones. A radial nearest neighbor is designed to select the surrounding nearest neighbors for each point, and these neighbors are analyzed to define the parameters of a local translational 3D plane surface. Experimental results using ISPRS benchmark datasets show that MIT achieves an average total error of <4.0 % , and an average kappa coefficient of >85 % . Further experimental validation with Hong Kong LiDAR datasets reveals that MIT is effective in detecting dense ground points and robust in various terrain situations.
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