scholarly journals A Fast Spatial Clustering Method for Sparse LiDAR Point Clouds Using GPU Programming

Sensors ◽  
2020 ◽  
Vol 20 (8) ◽  
pp. 2309
Author(s):  
Yifei Tian ◽  
Wei Song ◽  
Long Chen ◽  
Yunsick Sung ◽  
Jeonghoon Kwak ◽  
...  
Keyword(s):  
Point Cloud ◽  
Spatial Clustering ◽  
Point Clouds ◽  
Obstacle Detection ◽  
Small Cells ◽  
Gpu Programming ◽  
Processing Unit ◽  
Height Distribution ◽  
Connected Component ◽  
Accurate Perception

Fast and accurate obstacle detection is essential for accurate perception of mobile vehicles’ environment. Because point clouds sensed by light detection and ranging (LiDAR) sensors are sparse and unstructured, traditional obstacle clustering on raw point clouds are inaccurate and time consuming. Thus, to achieve fast obstacle clustering in an unknown terrain, this paper proposes an elevation-reference connected component labeling (ER-CCL) algorithm using graphic processing unit (GPU) programing. LiDAR points are first projected onto a rasterized x–z plane so that sparse points are mapped into a series of regularly arranged small cells. Based on the height distribution of the LiDAR point, the ground cells are filtered out and a flag map is generated. Next, the ER-CCL algorithm is implemented on the label map generated from the flag map to mark individual clusters with unique labels. Finally, obstacle labeling results are inverse transformed from the x–z plane to 3D points to provide clustering results. For real-time 3D point cloud clustering, ER-CCL is accelerated by running it in parallel with the aid of GPU programming technology.

scholarly journals Point cloud clustering and outlier detection based on spatial neighbor connected region labeling

2020 ◽  
pp. 002029402091986
Author(s):  
Xiaocui Yuan ◽  
Huawei Chen ◽  
Baoling Liu
Keyword(s):  
Outlier Detection ◽  
Point Cloud ◽  
Spatial Clustering ◽  
Clustering Algorithms ◽  
Large Data ◽  
Point Clouds ◽  
Detection Methods ◽  
Connected Region ◽  
Computational Time ◽  
Region Labeling

Clustering analysis is one of the most important techniques in point cloud processing, such as registration, segmentation, and outlier detection. However, most of the existing clustering algorithms exhibit a low computational efficiency with the high demand for computational resources, especially for large data processing. Sometimes, clusters and outliers are inseparable, especially for those point clouds with outliers. Most of the cluster-based algorithms can well identify cluster outliers but sparse outliers. We develop a novel clustering method, called spatial neighborhood connected region labeling. The method defines spatial connectivity criterion, finds points connections based on the connectivity criterion among the k-nearest neighborhood region and classifies connected points to the same cluster. Our method can accurately and quickly classify datasets using only one parameter k. Comparing with K-means, hierarchical clustering and density-based spatial clustering of applications with noise methods, our method provides better accuracy using less computational time for data clustering. For applications in the outlier detection of the point cloud, our method can identify not only cluster outliers, but also sparse outliers. More accurate detection results are achieved compared to the state-of-art outlier detection methods, such as local outlier factor and density-based spatial clustering of applications with noise.

scholarly journals A Novel Recursive Non-Parametric DBSCAN Algorithm for 3D Data Analysis with an Application in Rockfall Detection

2021 ◽  
Vol 16 (4) ◽  
pp. 579-587
Author(s):  
Pitisit Dillon ◽  
Pakinee Aimmanee ◽  
Akihiko Wakai ◽  
Go Sato ◽  
Hoang Viet Hung ◽  
...  
Keyword(s):  
Point Cloud ◽  
Crack Detection ◽  
Spatial Clustering ◽  
Point Clouds ◽  
Divide And Conquer ◽  
Dbscan Algorithm ◽  
3D Data ◽  
Data Points ◽  
Clustering Data ◽  
Better Than

The density-based spatial clustering of applications with noise (DBSCAN) algorithm is a well-known algorithm for spatial-clustering data point clouds. It can be applied to many applications, such as crack detection, rockfall detection, and glacier movement detection. Traditional DBSCAN requires two predefined parameters. Suitable values of these parameters depend upon the distribution of the input point cloud. Therefore, estimating these parameters is challenging. This paper proposed a new version of DBSCAN that can automatically customize the parameters. The proposed method consists of two processes: initial parameter estimation based on grid analysis and DBSCAN based on the divide-and-conquer (DC-DBSCAN) approach, which repeatedly performs DBSCAN on each cluster separately and recursively. To verify the proposed method, we applied it to a 3D point cloud dataset that was used to analyze rockfall events at the Puiggcercos cliff, Spain. The total number of data points used in this study was 15,567. The experimental results show that the proposed method is better than the traditional DBSCAN in terms of purity and NMI scores. The purity scores of the proposed method and the traditional DBSCAN method were 96.22% and 91.09%, respectively. The NMI scores of the proposed method and the traditional DBSCAN method are 0.78 and 0.49, respectively. Also, it can detect events that traditional DBSCAN cannot detect.

Registration of 3D Point Clouds for Ship Block Measurement

2015 ◽  
Author(s):  
Sunghan Kim ◽  
Mingyu Kim ◽  
Jeongtae Lee ◽  
Jinhwi Pyo ◽  
Heeyoung Heo ◽  
...  
Keyword(s):  
Point Cloud ◽  
Measurement Data ◽  
User Interaction ◽  
Point Clouds ◽  
Processing Unit ◽  
Feature Points ◽  
Automatic Registration ◽  
Actual Measurement ◽  
Large Size ◽  
3D Point Clouds

In this paper, a software system for registration of point clouds is developed. The system consists of two modules for registration and user interaction. The registration module contains functions for manual and automatic registration. The manual method allows a user to select feature points or planes from the point clouds manually. The selected planes or features are then processed to establish the correspondence between the point clouds, and registration is performed to obtain one large point cloud. The automatic registration uses sphere targets. Sphere targets are attached to an object of interest. A scanner measures the object as well as the targets to produce point clouds, from which the targets are extracted using shape intrinsic properties. Then correspondence between the point clouds is obtained using the targets, and the registration is performed. The user interaction module provides a GUI environment which allows a user to navigate point clouds, to compute various features, to visualize point clouds and to select/unselect points interactively and the point-processing unit containing functions for filtering, estimation of geometric features, and various data structures for managing point clouds of large size. The developed system is tested with actual measurement data of various blocks in a shipyard.

scholarly journals Building Change Detection of Airborne Laser Scanning and Dense Image Matching Point Clouds using Height and Class Information

2021 ◽  
Vol 2 ◽  
pp. 1-14
Author(s):  
Florian Politz ◽  
Monika Sester ◽  
Claus Brenner
Keyword(s):  
Change Detection ◽  
Spatial Data ◽  
Image Matching ◽  
Point Cloud ◽  
Laser Scanning ◽  
Point Clouds ◽  
Airborne Laser Scanning ◽  
Height Distribution ◽  
Airborne Laser ◽  
Dense Image

Abstract. Detecting changes is an important task to update databases and find irregularities in spatial data. Every couple of years, national mapping agencies (NMAs) acquire nation-wide point cloud data from Airborne Laser Scanning (ALS) as well as from Dense Image Matching (DIM) using aerial images. Besides deriving several other products such as Digital Elevation Models (DEMs) from them, those point clouds also offer the chance to detect changes between two points in time on a large scale. Buildings are an important object class in the context of change detection to update cadastre data. As detecting changes manually is very time consuming, the aim of this study is to provide reliable change detections for different building sizes in order to support NMAs in their task to update their databases. As datasets of different times may have varying point densities due to technological advancements or different sensors, we propose a raster-based approach, which is independent of the point density altogether. Within a raster cell, our approach considers the height distribution of all points for two points in time by exploiting the Jensen-Shannon distance to measure their similarity. Our proposed method outperforms simple threshold methods on detecting building changes with respect to the same or different point cloud types. In combination with our proposed class change detection approach, we achieve a change detection performance measured by the mean F1-Score of about 71% between two ALS and about 60% between ALS and DIM point clouds acquired at different times.

scholarly journals Novel Laser-Based Obstacle Detection for Autonomous Robots on Unstructured Terrain

Sensors ◽  
2020 ◽  
Vol 20 (18) ◽  
pp. 5048
Author(s):  
Wei Chen ◽  
Qianjie Liu ◽  
Huosheng Hu ◽  
Jun Liu ◽  
Shaojie Wang ◽  
...  
Keyword(s):  
Point Cloud ◽  
Clustering Algorithm ◽  
Autonomous Robots ◽  
Back Propagation ◽  
Point Clouds ◽  
Obstacle Detection ◽  
Gaussian Kernel ◽  
Sobel Operator ◽  
And Performance ◽  
Edge Features

Obstacle detection is one of the essential capabilities for autonomous robots operated on unstructured terrain. In this paper, a novel laser-based approach is proposed for obstacle detection by autonomous robots, in which the Sobel operator is deployed in the edge-detection process of 3D laser point clouds. The point clouds of unstructured terrain are filtered by VoxelGrid, and then processed by the Gaussian kernel function to obtain the edge features of obstacles. The Euclidean clustering algorithm is optimized by super-voxel in order to cluster the point clouds of each obstacle. The characteristics of the obstacles are recognized by the Levenberg–Marquardt back-propagation (LM-BP) neural network. The algorithm proposed in this paper is a post-processing algorithm based on the reconstructed point cloud. Experiments are conducted by using both the existing datasets and real unstructured terrain point cloud reconstructed by an all-terrain robot to demonstrate the feasibility and performance of the proposed approach.

scholarly journals Semi-automatic LiDAR point cloud denoising using a connected-component labelling method

2019 ◽  
Vol 13 (2) ◽  
Author(s):  
Ján Kaňuk ◽  
Jozef Šupinský ◽  
Ján Šašak ◽  
Jaroslav Hofierka ◽  
Yongbo Wang ◽  
...  
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Urban Landscape ◽  
Point Clouds ◽  
Connected Component ◽  
New Approach ◽  
Data Noise ◽  
The City ◽  
Labelling Method ◽  
Automatic Elimination

The Smart City concept requires new, fast methods for collection of 3-D data representing features of urban landscape. Laser scanning technology (LiDAR - Light Detection and Ranging) enables such approach producing dense 3-D point clouds of millions of points, which, however, contain noise. Therefore, we developed a new approach allowing for a semi-automatic elimination of data noise resulting from motion of objects within the scanned scene such as persons. We used a connected-component labelling method to filter out the noise points from terrestrial laser scanning point clouds. Our approach was based on a step-by-step object classification with a proper parameterisation. In the first step, all points located close to the predicted terrain were selected. In the second step, the points representing the terrain and floor were classified using the surface filter tool implemented in the RiScan Pro software by RIEGL. The rest of points were classified using point cloud clustering via the connected-component labelling method implemented in the CloudCompare software. In the final step, the operator manually decides whether the point cluster represents the noise. The method was applied to the Cathedral of Saint Elizabeth, a sacral object located in the historical centre of the city of Košice in Slovakia during normal operating hours. We managed to capture approximately 80% of the data noise in total. The method provides a better flexibility in surveying overcrowded city locations using the laser scanning technology.

scholarly journals An Elevation Ambiguity Resolution Method Based on Segmentation and Reorganization of TomoSAR Point Cloud in 3D Mountain Reconstruction

2021 ◽  
Vol 13 (24) ◽  
pp. 5118
Author(s):  
Xiaowan Li ◽  
Fubo Zhang ◽  
Yanlei Li ◽  
Qichang Guo ◽  
Yangliang Wan ◽  
...  
Keyword(s):  
Ambiguity Resolution ◽  
Point Cloud ◽  
Spatial Clustering ◽  
Point Clouds ◽  
Gaussian Mixture ◽  
High Mountain ◽  
Real Point ◽  
Resolution Method ◽  
Mountain Areas ◽  
External Data

Tomographic Synthetic Aperture Radar (TomoSAR) is a breakthrough of the traditional SAR, which has the three-dimentional (3D) observation ability of layover scenes such as buildings and high mountains. As an advanced system, the airborne array TomoSAR can effectively avoid temporal de-correlation caused by long revisit time, which has great application in high-precision mountain surveying and mapping. The 3D reconstruction using TomoSAR has mainly focused on low targets, while there are few literatures on 3D mountain reconstruction. Due to the layover phenomenon, surveying in high mountain areas remains a difficult task. Consequently, it is meaningful to carry out the research on 3D mountain reconstruction using the airborne array TomoSAR. However, the original TomoSAR mountain point cloud faces the problem of elevation ambiguity. Furthermore, for mountains with complex terrain, the points located in different elevation periods may intersect. This phenomenon increases the difficulty of solving the problem. In this paper, a novel elevation ambiguity resolution method is proposed. First, Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and Gaussian Mixture Model (GMM) are combined for point cloud segmentation. The former ensures coarse segmentation based on density, and the latter allows fine segmentation of the abnormal categories caused by intersection. Subsequently, the segmentation results are reorganized in the elevation direction to reconstruct all possible point clouds. Finally, the real point cloud can be extracted automatically under the constraints of the boundary and elevation continuity. The performance of the proposed method is demonstrated by simulations and experiments. Based on the airborne array TomoSAR experiment in Leshan City, Sichuan Province, China in 2019, the 3D model of the surveyed mountain is presented. Moreover, three kinds of external data are applied to fully verify the validity of this method.

A Highly Parallel Approach to Meshing Scattered Point Data

2009 ◽  
Author(s):  
Hoi Sheung ◽  
Siu-Ping Mok ◽  
Charlie C. L. Wang
Keyword(s):  
Point Cloud ◽  
Graphics Processing Unit ◽  
Approximation Error ◽  
Point Clouds ◽  
Surface Generation ◽  
Graphics Hardware ◽  
Processing Unit ◽  
Mesh Surface ◽  
Normal Vectors ◽  
Point Cloud Simplification

In this paper, we present a parallel mesh surface generation approach for unorganized point clouds that runs on the graphics processing unit (GPU). Our approach integrates point cloud simplification, point cloud optimization, and local triangulation techniques into the same framework. The input point cloud will be processed through three steps of algorithms, which are 1) preprocessing: to generate the neighborhood table of points and estimate the normal vectors, 2) clustering: to group points into optimized clusters that minimize the shape approximation error, and 3) meshing: to connect the seed points in clusters to form the resultant triangular mesh surface. As the number of clusters can be specified by users, the number of vertices on resultant mesh surfaces is controlled. The algorithms exploited here are highly parallelized to take advantage of the single-instruction-multiple-data (SIMD) parallelism that is available on consumer-level graphics hardware with GPU. Moreover, to overcome memory limitation on graphics hardware, the algorithms in all these steps are able to process massive data in streaming mode.

Registration and Fusion of UAV LiDAR System Sequence Images and Laser Point Clouds

2020 ◽  
Author(s):  
Jiayong Yu ◽  
Longchen Ma ◽  
Maoyi Tian, ◽  
Xiushan Lu
Keyword(s):  
Point Cloud ◽  
Image Data ◽  
Point Clouds ◽  
Optical Image ◽  
Point Cloud Data ◽  
Feature Points ◽  
Lidar System ◽  
Registration Accuracy ◽  
Cloud Data ◽  
Intensity Image

The unmanned aerial vehicle (UAV)-mounted mobile LiDAR system (ULS) is widely used for geomatics owing to its efficient data acquisition and convenient operation. However, due to limited carrying capacity of a UAV, sensors integrated in the ULS should be small and lightweight, which results in decrease in the density of the collected scanning points. This affects registration between image data and point cloud data. To address this issue, the authors propose a method for registering and fusing ULS sequence images and laser point clouds, wherein they convert the problem of registering point cloud data and image data into a problem of matching feature points between the two images. First, a point cloud is selected to produce an intensity image. Subsequently, the corresponding feature points of the intensity image and the optical image are matched, and exterior orientation parameters are solved using a collinear equation based on image position and orientation. Finally, the sequence images are fused with the laser point cloud, based on the Global Navigation Satellite System (GNSS) time index of the optical image, to generate a true color point cloud. The experimental results show the higher registration accuracy and fusion speed of the proposed method, thereby demonstrating its accuracy and effectiveness.

scholarly journals 3D Thermal Monitoring of Jointed Rock Masses through Infrared Thermography and Photogrammetry

2021 ◽  
Vol 13 (5) ◽  
pp. 957
Author(s):  
Guglielmo Grechi ◽  
Matteo Fiorucci ◽  
Gian Marco Marmoni ◽  
Salvatore Martino
Keyword(s):  
Surface Temperature ◽  
Infrared Thermography ◽  
Point Cloud ◽  
Engineering Geology ◽  
Point Clouds ◽  
Jointed Rock ◽  
Geometric Accuracy ◽  
Rock Masses ◽  
Natural Systems ◽  
Temperature Distributions

The study of strain effects in thermally-forced rock masses has gathered growing interest from engineering geology researchers in the last decade. In this framework, digital photogrammetry and infrared thermography have become two of the most exploited remote surveying techniques in engineering geology applications because they can provide useful information concerning geomechanical and thermal conditions of these complex natural systems where the mechanical role of joints cannot be neglected. In this paper, a methodology is proposed for generating point clouds of rock masses prone to failure, combining the high geometric accuracy of RGB optical images and the thermal information derived by infrared thermography surveys. Multiple 3D thermal point clouds and a high-resolution RGB point cloud were separately generated and co-registered by acquiring thermograms at different times of the day and in different seasons using commercial software for Structure from Motion and point cloud analysis. Temperature attributes of thermal point clouds were merged with the reference high-resolution optical point cloud to obtain a composite 3D model storing accurate geometric information and multitemporal surface temperature distributions. The quality of merged point clouds was evaluated by comparing temperature distributions derived by 2D thermograms and 3D thermal models, with a view to estimating their accuracy in describing surface thermal fields. Moreover, a preliminary attempt was made to test the feasibility of this approach in investigating the thermal behavior of complex natural systems such as jointed rock masses by analyzing the spatial distribution and temporal evolution of surface temperature ranges under different climatic conditions. The obtained results show that despite the low resolution of the IR sensor, the geometric accuracy and the correspondence between 2D and 3D temperature measurements are high enough to consider 3D thermal point clouds suitable to describe surface temperature distributions and adequate for monitoring purposes of jointed rock mass.

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