Due to the complexity of surrounding environments, lidar point cloud data (PCD) are often degraded by plane noise. In order to eliminate noise, this paper proposes a filtering scheme based on the grid principal component analysis (PCA) technique and the ground splicing method. The 3D PCD is first projected onto a desired 2D plane, within which the ground and wall data are well separated from the PCD via a prescribed index based on the statistics of points in all 2D mesh grids. Then, a KD-tree is constructed for the ground data, and rough segmentation in an unsupervised method is conducted to obtain the true ground data by using the normal vector as a distinctive feature. To improve the performance of noise removal, we propose an elaborate K nearest neighbor (KNN)-based segmentation method via an optimization strategy. Finally, the denoised data of the wall and ground are spliced for further 3D reconstruction. The experimental results show that the proposed method is efficient at noise removal and is superior to several traditional methods in terms of both denoising performance and run speed.

In this work, we propose a new light detection and ranging (LiDAR) super-resolution method for indoor and outdoor scenes in urban environments without training, converting to images and auxiliary sensors. To generate high-resolution (high-res) point clouds from the low-resolution (low-res) measurement of a sparse LiDAR, we deconstruct the whole problem into three sequential modules. First, to avoid feature interference, the raw data are segmented into the ground and nonground points. Then, these two types of points are reorganized into a more regular representation of the coordinates. Finally, the high-res point clouds are generated via a new geometric method. We present abundant synthetic and real data based on testing and evaluation of the proposed method. Qualitative and quantitative comparisons show that our method is effective and robust.

We propose an innovative method with which to extract building interior structure information automatically, including ceiling, floor, and wall. Our approach outperforms previous methods in the following respects. First, we propose an approach based on principal component analysis (PCA) to find the ground plane, which is regarded as the new Cartesian plane. Second, to reduce the complexity of data processing, the data are projected into two dimensions and transformed into a binary image via the operation of an improved radius outlier removal (ROR) filter. Third, a traditional thinning algorithm is adopted to extract the image skeleton. Then, we propose a method for calculating slope through the nearest neighbor point. Moreover, the line is represented with the slopes to obtain information pertaining to the interior planes. Finally, the outline of the line is restored to a three-dimensional structure. The proposed method is evaluated in multiple scenarios, and the results show that the method is accurate (the maximum error of 0.03 m was in three scenarios) in indoor environments.

To improve the efficiency and accuracy of plane segmentation for indoor point clouds, this paper proposes a fast and precise plane segmentation framework which mainly consists of two steps: plane rough segmentation and precise segmentation. In the rough segmentation stage, the point clouds are firstly voxelized, then the original plane is extracted roughly according to the plane normal vector and nearest voxels conditions. Based on the results of rough segmentation, a further operation composed of downsampling and density-based spatial clustering of applications with noise (DBSCAN) is adopted to produce efficient and precise segmentation. Finally, to correct the over-segmentation, the distance and normal vector angle thresholds between planes are taken into consideration. The experimental results show that the proposed method improves the efficiency and accuracy of indoor point cloud plane segmentation, and the average intersection-over-union (IoU) achieves 0.8653.

Most studies on map segmentation and recognition are focused on architectural floor plans, while there are very few analyses of shopping mall plans. The objective of the work is to accurately segment and recognize the shopping mall plan, obtaining location and semantic information for each room via segmentation and recognition. This work can be used in other applications such as indoor robot navigation, building area and location analysis, and three-dimensional reconstruction. First, we identify and match the catalog of a mall floor plan to obtain matching text, and then we use the two-stage region growth method we proposed to segment the preprocessed floor plan. The room number is then obtained by sending each segmented room section to an OCR (optical character recognition) system for identification. Finally, the system retrieves the matching text to match the room number in order to obtain the room name, and outputs the needed room location and semantic information. It is considered a successful detection when a room region can be successfully segmented and identified. The proposed method is evaluated on a dataset including 1340 rooms. Experimental results show that the accuracy of room segmentation is 92.54%, and the accuracy of room recognition is 90.56%. The total detection accuracy is 83.81%.