Unsupervised Segmentation in 3D Planar Object Maps Based on Fuzzy Clustering

This paper investigates the problem of acquiring planar object maps of indoor household environments in particular kitchens. The objects modeled in these maps include tables, walls and ceilings. Our segmentation approach is based on 3D point cloud data representations. In order to solve the segmentation problem in complicated environment, a variable model is used in this paper. It is applied in 3D planar segmentation from point clouds. The segmentation algorithm was developed based on fuzzy K-means, which can automatically find the optimal number of clusters and self-organize the clusters based on Inter cluster Distance Index and Sample Distribution Index. After the 3D point clouds of each planar are clustered, we get the 3D planar candidates by extracting semantic information from the clusters based on functional reasoning module. The model has been evaluated on the real test scenes, which contain noisy point clouds. Empirical results show that our model can rationally interpret planar objects from the point clouds and shows the high performance and robust results.