Rapid Motion Segmentation of LiDAR Point Cloud Based on a Combination of Probabilistic and Evidential Approaches for Intelligent Vehicles

Point clouds from light detecting and ranging (LiDAR) sensors represent increasingly important information for environmental object detection and classification of automated and intelligent vehicles. Objects in the driving environment can be classified as either d y n a m i c or s t a t i c depending on their movement characteristics. A LiDAR point cloud is also segmented into d y n a m i c and s t a t i c points based on the motion properties of the measured objects. The segmented motion information of a point cloud can be useful for various functions in automated and intelligent vehicles. This paper presents a fast motion segmentation algorithm that segments a LiDAR point cloud into d y n a m i c and s t a t i c points in real-time. The segmentation algorithm classifies the motion of the latest point cloud based on the LiDAR’s laser beam characteristics and the geometrical relationship between consecutive LiDAR point clouds. To accurately and reliably estimate the motion state of each LiDAR point considering the measurement uncertainty, both probability theory and evidence theory are employed in the segmentation algorithm. The probabilistic and evidential algorithm segments the point cloud into three classes: d y n a m i c , s t a t i c , and u n k n o w n . Points are placed in the u n k n o w n class when LiDAR point cloud is not sufficient for motion segmentation. The point motion segmentation algorithm was evaluated quantitatively and qualitatively through experimental comparisons with previous motion segmentation methods.

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A Kernel-Based Change Detection Method to Map Shifts in Phytoplankton Communities Measured by Flow Cytometry

10.1101/2020.12.01.405126 ◽

2020 ◽

Author(s):

Corinne Jones ◽

Sophie Clayton ◽

François Ribalet ◽

E. Virginia Armbrust ◽

Zaid Harchaoui

Keyword(s):

Flow Cytometry ◽

Community Structure ◽

Change Point ◽

Point Cloud ◽

Point Clouds ◽

Structure Identification ◽

Point Cloud Data ◽

Cloud Data ◽

Flow Cytometry Data ◽

Segmentation Methods

SummaryAutomated, ship-board flow cytometers provide high-resolution maps of phytoplankton composition over large swaths of the world’s oceans. They therefore pave the way for understanding how environmental conditions shape community structure. Identification of community changes along a cruise transect commonly segments the data into distinct regions. However, existing segmentation methods are generally not applicable to flow cytometry data, as this data is recorded as “point cloud” data, with hundreds or thousands of particles measured during each time interval. Moreover, nonparametric segmentation methods that do not rely on prior knowledge of the number of species, are desirable to map community shifts.We present CytoSegmenter, a kernel-based change-point estimation method for segmenting point cloud data that does not rely on parametric assumptions on the data distributions. Our method relies on a Hilbertian embedding of point clouds that allows us to work with point cloud data similarly to vectorial data. The change-point locations can be found using an efficient dynamic programming algorithm. The method can be used to automatically segment long series of underway flow cytometry data.Through an analysis of 12 cruises, we demonstrate that CytoSegmenter allows us to locate abrupt changes in phytoplankton community structure. We show that the changes in community structure generally coincide with changes in the temperature and salinity of the ocean. We also illustrate how the main parameter of CytoSegmenter can be easily calibrated using limited auxiliary annotated data.CytoSegmenter is publicly available and implemented in the programming language Python. The method is generally applicable for segmenting series of point cloud data from any domain. Moreover, it readily scales to thousands of point clouds, each containing thousands of points. In the context of underway flow cytometry data, it does not require prior clustering of particles to define taxa labels, eliminating a potential source of error. This represents an important advance in automating the analysis of large datasets now emerging in biological oceanography and other fields. It also allows for the approach to potentially be applied during research cruises.

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SEGMENTATION OF 3D PHOTOGRAMMETRIC POINT CLOUD FOR 3D BUILDING MODELING

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-4-w10-135-2018 ◽

2018 ◽

Vol XLII-4/W10 ◽

pp. 135-142 ◽

Cited By ~ 2

Author(s):

E. Özdemir ◽

F. Remondino

Keyword(s):

Point Cloud ◽

Ground Level ◽

Point Clouds ◽

Energy Evaluation ◽

3D Point Clouds ◽

Building Modeling ◽

3D Cadastre ◽

Segmentation Methods ◽

The One

Abstract. 3D city modeling has become important over the last decades as these models are being used in different studies including, energy evaluation, visibility analysis, 3D cadastre, urban planning, change detection, disaster management, etc. Segmentation and classification of photogrammetric or LiDAR data is important for 3D city models as these are the main data sources, and, these tasks are challenging due to their complexity. This study presents research in progress, which focuses on the segmentation and classification of 3D point clouds and orthoimages to generate 3D urban models. The aim is to classify photogrammetric-based point clouds (&gt;30pts/sqm) in combination with aerial RGB orthoimages (~10cm, RGB image) in order to name buildings, ground level objects (GLOs), trees, grass areas, and other regions. If on the one hand the classification of aerial orthoimages is foreseen to be a fast approach to get classes and then transfer them from the image to the point cloud space, on the other hand, segmenting a point cloud is expected to be much more time consuming but to provide significant segments from the analyzed scene. For this reason, the proposed method combines segmentation methods on the two geoinformation in order to achieve better results.

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VOXEL- AND GRAPH-BASED POINT CLOUD SEGMENTATION OF 3D SCENES USING PERCEPTUAL GROUPING LAWS

ISPRS Annals of Photogrammetry Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-annals-iv-1-w1-43-2017 ◽

2017 ◽

Vol IV-1/W1 ◽

pp. 43-50 ◽

Cited By ~ 11

Author(s):

Y. Xu ◽

L. Hoegner ◽

S. Tuttas ◽

U. Stilla

Keyword(s):

Perceptual Grouping ◽

Point Cloud ◽

Contextual Information ◽

Clustering Algorithms ◽

Point Clouds ◽

Point Cloud Segmentation ◽

Intensity Information ◽

Segmentation Methods ◽

Negative Effect ◽

3D Scene

Segmentation is the fundamental step for recognizing and extracting objects from point clouds of 3D scene. In this paper, we present a strategy for point cloud segmentation using voxel structure and graph-based clustering with perceptual grouping laws, which allows a learning-free and completely automatic but parametric solution for segmenting 3D point cloud. To speak precisely, two segmentation methods utilizing voxel and supervoxel structures are reported and tested. The voxel-based data structure can increase efficiency and robustness of the segmentation process, suppressing the negative effect of noise, outliers, and uneven points densities. The clustering of voxels and supervoxel is carried out using graph theory on the basis of the local contextual information, which commonly conducted utilizing merely pairwise information in conventional clustering algorithms. By the use of perceptual laws, our method conducts the segmentation in a pure geometric way avoiding the use of RGB color and intensity information, so that it can be applied to more general applications. Experiments using different datasets have demonstrated that our proposed methods can achieve good results, especially for complex scenes and nonplanar surfaces of objects. Quantitative comparisons between our methods and other representative segmentation methods also confirms the effectiveness and efficiency of our proposals.

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PAIRWISE LINKAGE FOR POINT CLOUD SEGMENTATION

ISPRS Annals of Photogrammetry Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-annals-iii-3-201-2016 ◽

2016 ◽

Vol III-3 ◽

pp. 201-208 ◽

Cited By ~ 1

Author(s):

Xiaohu Lu ◽

Jian Yao ◽

Jinge Tu ◽

Kai Li ◽

Li Li ◽

...

Keyword(s):

Point Cloud ◽

Clustering Algorithm ◽

Input Parameter ◽

Point Clouds ◽

Segmentation Algorithm ◽

Experimental Results ◽

3D Point Cloud ◽

3D Point Clouds ◽

Point Cloud Segmentation ◽

Data Point

In this paper, we first present a novel hierarchical clustering algorithm named Pairwise Linkage (P-Linkage), which can be used for clustering any dimensional data, and then effectively apply it on 3D unstructured point cloud segmentation. The P-Linkage clustering algorithm first calculates a feature value for each data point, for example, the density for 2D data points and the flatness for 3D point clouds. Then for each data point a pairwise linkage is created between itself and its closest neighboring point with a greater feature value than its own. The initial clusters can further be discovered by searching along the linkages in a simple way. After that, a cluster merging procedure is applied to obtain the finally refined clustering result, which can be designed for specialized applications. Based on the P-Linkage clustering, we develop an efficient segmentation algorithm for 3D unstructured point clouds, in which the flatness of the estimated surface of a 3D point is used as its feature value. For each initial cluster a slice is created, then a novel and robust slicemerging method is proposed to get the final segmentation result. The proposed P-Linkage clustering and 3D point cloud segmentation algorithms require only one input parameter in advance. Experimental results on different dimensional synthetic data from 2D to 4D sufficiently demonstrate the efficiency and robustness of the proposed P-Linkage clustering algorithm and a large amount of experimental results on the Vehicle-Mounted, Aerial and Stationary Laser Scanner point clouds illustrate the robustness and efficiency of our proposed 3D point cloud segmentation algorithm.

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PAIRWISE LINKAGE FOR POINT CLOUD SEGMENTATION

ISPRS Annals of Photogrammetry Remote Sensing and Spatial Information Sciences ◽

10.5194/isprsannals-iii-3-201-2016 ◽

2016 ◽

Vol III-3 ◽

pp. 201-208 ◽

Cited By ~ 13

Author(s):

Xiaohu Lu ◽

Jian Yao ◽

Jinge Tu ◽

Kai Li ◽

Li Li ◽

...

Keyword(s):

Point Cloud ◽

Clustering Algorithm ◽

Input Parameter ◽

Point Clouds ◽

Segmentation Algorithm ◽

Experimental Results ◽

3D Point Cloud ◽

3D Point Clouds ◽

Point Cloud Segmentation ◽

Data Point

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

Journal of Imaging Science and Technology ◽

10.2352/j.imagingsci.technol.2021.65.1.01050110.2352/j.imagingsci.technol.2021.65.1.010501 ◽

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.

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Assessment of automatic segmentation accuracy with various point cloud density

Geodesy and Cartography ◽

10.22389/0016-7126-2020-961-7-47-55 ◽

2020 ◽

Vol 961 (7) ◽

pp. 47-55

Author(s):

A.G. Yunusov ◽

A.J. Jdeed ◽

N.S. Begliarov ◽

M.A. Elshewy

Keyword(s):

Point Cloud ◽

Laser Scanning ◽

Reference Data ◽

Automatic Segmentation ◽

Geometric Accuracy ◽

Segmentation Method ◽

Segmentation Methods ◽

Segmentation Accuracy ◽

Number Of Segments ◽

Cloud Density

Laser scanning is considered as one of the most useful and fast technologies for modelling. On the other hand, the size of scan results can vary from hundreds to several million points. As a result, the large volume of the obtained clouds leads to complication at processing the results and increases the time costs. One way to reduce the volume of a point cloud is segmentation, which reduces the amount of data from several million points to a limited number of segments. In this article, we evaluated effect on the performance, the accuracy of various segmentation methods and the geometric accuracy of the obtained models at density changes taking into account the processing time. The results of our experiment were compared with reference data in a form of comparative analysis. As a conclusion, some recommendations for choosing the best segmentation method were proposed.

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

Remote Sensing ◽

10.3390/rs13050957 ◽

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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Linear-Based Incremental Co-Registration of MLS and Photogrammetric Point Clouds

Remote Sensing ◽

10.3390/rs13112195 ◽

2021 ◽

Vol 13 (11) ◽

pp. 2195

Author(s):

Shiming Li ◽

Xuming Ge ◽

Shengfu Li ◽

Bo Xu ◽

Zhendong Wang

Keyword(s):

Point Cloud ◽

Laser Scanning ◽

Point Clouds ◽

Registration Method ◽

Cloud Data ◽

Linear Feature ◽

Data Registration ◽

Source Point ◽

Urban Remote Sensing ◽

Single Data

Today, mobile laser scanning and oblique photogrammetry are two standard urban remote sensing acquisition methods, and the cross-source point-cloud data obtained using these methods have significant differences and complementarity. Accurate co-registration can make up for the limitations of a single data source, but many existing registration methods face critical challenges. Therefore, in this paper, we propose a systematic incremental registration method that can successfully register MLS and photogrammetric point clouds in the presence of a large number of missing data, large variations in point density, and scale differences. The robustness of this method is due to its elimination of noise in the extracted linear features and its 2D incremental registration strategy. There are three main contributions of our work: (1) the development of an end-to-end automatic cross-source point-cloud registration method; (2) a way to effectively extract the linear feature and restore the scale; and (3) an incremental registration strategy that simplifies the complex registration process. The experimental results show that this method can successfully achieve cross-source data registration, while other methods have difficulty obtaining satisfactory registration results efficiently. Moreover, this method can be extended to more point-cloud sources.

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Parametric Surface Modelling for Tea Leaf Point Cloud Based on Non-Uniform Rational Basis Spline Technique

Sensors ◽

10.3390/s21041304 ◽

2021 ◽

Vol 21 (4) ◽

pp. 1304

Author(s):

Wenchao Wu ◽

Yongguang Hu ◽

Yongzong Lu

Keyword(s):

Point Cloud ◽

Principal Component ◽

Point Clouds ◽

Parametric Surface ◽

B Spline ◽

Surface Modelling ◽

Modelling Method ◽

3D Architecture ◽

Tea Leaf ◽

Fitting Error

Plant leaf 3D architecture changes during growth and shows sensitive response to environmental stresses. In recent years, acquisition and segmentation methods of leaf point cloud developed rapidly, but 3D modelling leaf point clouds has not gained much attention. In this study, a parametric surface modelling method was proposed for accurately fitting tea leaf point cloud. Firstly, principal component analysis was utilized to adjust posture and position of the point cloud. Then, the point cloud was sliced into multiple sections, and some sections were selected to generate a point set to be fitted (PSF). Finally, the PSF was fitted into non-uniform rational B-spline (NURBS) surface. Two methods were developed to generate the ordered PSF and the unordered PSF, respectively. The PSF was firstly fitted as B-spline surface and then was transformed to NURBS form by minimizing fitting error, which was solved by particle swarm optimization (PSO). The fitting error was specified as weighted sum of the root-mean-square error (RMSE) and the maximum value (MV) of Euclidean distances between fitted surface and a subset of the point cloud. The results showed that the proposed modelling method could be used even if the point cloud is largely simplified (RMSE < 1 mm, MV < 2 mm, without performing PSO). Future studies will model wider range of leaves as well as incomplete point cloud.

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