Near real-time point cloud processing using the PCL

Miknis, Marius; Davies, Ross; Plassmann, P.; Ware, Andrew

doi:10.1109/iwssip.2015.7314200

Cited by 28 publications

(19 citation statements)

References 5 publications

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“…For this case, the fast statistical outlier removal (FSOR) proposed by Balta et al [51] was used to minimize the processing time and optimize resources. After cleaning the point cloud, a passthrough filter [52,53] based on the coordinate histogram of the point clouds is applied. Because the Faro Focus captures data in the range 0.60-330 m, the system measures points that are too far from the scan station and consequently useless for the application (Figure 5a,c).…”

Section: Pre-processingmentioning

confidence: 99%

Automatic Point-Cloud Registration for Quality Control in Building Works

et al. 2021

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Total and automatic digitalization of indoor spaces in 3D implies a great advance in building maintenance and construction tasks, which currently require visits and manual works. Terrestrial laser scanners (TLS) have been widely used for these tasks, although the acquisition methodology with TLS systems is time consuming, and each point cloud is acquired in a different coordinate system, so the user has to post-process the data to clean and get a unique point cloud of the whole scenario. This paper presents a solution for the automatic data acquisition and registration of point clouds from indoor scenes, designed for point clouds acquired with a terrestrial laser scanner (TLS) mounted on an unmanned ground vehicle (UGV). The methodology developed allows the generation of one complete dense 3D point cloud consisting of the acquired point clouds registered in the same coordinate system, reaching an accuracy below 1 cm in section dimensions and below 1.5 cm in walls thickness, which makes it valid for quality control in building works. Two different study cases corresponding to building works were chosen for the validation of the method, showing the applicability of the methodology developed for tasks related to the control of the evolution of the construction.

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Section: Pre-processingmentioning

confidence: 99%

Automatic Point-Cloud Registration for Quality Control in Building Works

et al. 2021

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“…It is necessary to filter the point cloud after the acquisition. PassThrough filter [21] is to remove noise points from the point cloud which are not within a specific range by setting the upper and lower limits. According to the coordinate system characteristics of the Kinect V2 depth camera and range of accuracy [20] , 0.5 and 2.5 m from the camera to the object were respectively set as the lower and upper distance limits for PassThrough filtering.…”

Section: Point Cloud Preprocessingmentioning

confidence: 99%

Point cloud registration for agriculture and forestry crops based on calibration balls using Kinect V2

Zhou

Kang

et al. 2020

International Journal of Agricultural and Biological Engineering

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For the process of point cloud registration, and the problem of inaccurate registration due to errors in correspondence between keypoints. In this paper, a registration method based on calibration balls was proposed, the trunk, branch, and crown were selected as experimental objects, and three calibration balls were randomly placed around the experimental objects to ensure different distances between two ball centers. Using the Kinect V2 depth camera to collect the point cloud of the experimental scene from four different viewpoints, the PassThrough filter algorithm was used for point cloud filtering in each view of the experimental scenes. The Euclidean cluster extraction algorithm was employed for point cloud clustering and segmentation to extract the experimental object and the calibration ball. The random sample consensus (RANSAC) algorithm was applied to fit the point cloud of a ball and calculate the coordinates of the ball center so that the distance between two ball centers under different viewpoints can be obtained by using the coordinates of the ball center. Comparing the distance between the ball centers from different viewpoints to determine the corresponding relationship between the ball centers from different viewpoints, and then using the singular value decomposition (SVD) method, the initial registration matrix was obtained. Finally, Iterative Closest Point (ICP) and its improved algorithm were used for accurate registration. The experimental results showed that the method of point cloud registration based on calibration balls can solve the problem of corresponding error of keypoints, and can register point clouds from different viewpoints of the same object. The registration method was evaluated by using the registration running time and the fitness score. The final registration running time of different experimental objects was not more than 6.5 s. The minimum fitness score of the trunk was approximately 0.0001, the minimum fitness score of the branch was approximately 0.0001, and the minimum fitness score of the crown was approximately 0.0006.

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“…triangulation algorithm, the greedy projection triangulation reveals the geometric features on point clouds clearly. Except for FPPS, the other simplification algorithms-uniform-based [42], curvaturebased [43], and grid-based [44] simplification algorithms-were performed by Geomatic at the same simplification ratio. In this experiment, we executed point cloud simplification on different data sets and analyzed the geometric features saved in S. Because of the geometric features' diversity, we classified the data sets into three types.…”

Section: Point Cloud Simplification For Natural Data Setsmentioning

confidence: 99%

Feature-Preserved Point Cloud Simplification Based on Natural Quadric Shape Models

et al. 2019

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With the development of 3D scanning technology, a huge volume of point cloud data has been collected at a lower cost. The huge data set is the main burden during the data processing of point clouds, so point cloud simplification is critical. The main aim of point cloud simplification is to reduce data volume while preserving the data features. Therefore, this paper provides a new method for point cloud simplification, named FPPS (feature-preserved point cloud simplification). In FPPS, point cloud simplification entropy is defined, which quantifies features hidden in point clouds. According to simplification entropy, the key points including the majority of the geometric features are selected. Then, based on the natural quadric shape, we introduce a point cloud matching model (PCMM), by which the simplification rules are set. Additionally, the similarity between PCMM and the neighbors of the key points is measured by the shape operator. This represents the criteria for the adaptive simplification parameters in FPPS. Finally, the experiment verifies the feasibility of FPPS and compares FPPS with other four-point cloud simplification algorithms. The results show that FPPS is superior to other simplification algorithms. In addition, FPPS can partially recognize noise.

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Near real-time point cloud processing using the PCL

Cited by 28 publications

References 5 publications

Automatic Point-Cloud Registration for Quality Control in Building Works

Automatic Point-Cloud Registration for Quality Control in Building Works

Point cloud registration for agriculture and forestry crops based on calibration balls using Kinect V2

Feature-Preserved Point Cloud Simplification Based on Natural Quadric Shape Models

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