scholarly journals 3-D Point Cloud Registration Using Convolutional Neural Networks

2019 ◽  
Vol 9 (16) ◽  
pp. 3273 ◽  
Author(s):  
Wen-Chung Chang ◽  
Van-Toan Pham
Keyword(s):  
Pose Estimation ◽  
Point Cloud ◽  
Full Range ◽  
Point Clouds ◽  
Fixed Time ◽  
Training Data ◽  
Data Sets ◽  
Point Cloud Registration ◽  
Cloud Data ◽  
Model Point

This paper develops a registration architecture for the purpose of estimating relative pose including the rotation and the translation of an object in terms of a model in 3-D space based on 3-D point clouds captured by a 3-D camera. Particularly, this paper addresses the time-consuming problem of 3-D point cloud registration which is essential for the closed-loop industrial automated assembly systems that demand fixed time for accurate pose estimation. Firstly, two different descriptors are developed in order to extract coarse and detailed features of these point cloud data sets for the purpose of creating training data sets according to diversified orientations. Secondly, in order to guarantee fast pose estimation in fixed time, a seemingly novel registration architecture by employing two consecutive convolutional neural network (CNN) models is proposed. After training, the proposed CNN architecture can estimate the rotation between the model point cloud and a data point cloud, followed by the translation estimation based on computing average values. By covering a smaller range of uncertainty of the orientation compared with a full range of uncertainty covered by the first CNN model, the second CNN model can precisely estimate the orientation of the 3-D point cloud. Finally, the performance of the algorithm proposed in this paper has been validated by experiments in comparison with baseline methods. Based on these results, the proposed algorithm significantly reduces the estimation time while maintaining high precision.

scholarly journals Large Common Plansets-4-Points Congruent Sets for Point Cloud Registration

2020 ◽  
Vol 9 (11) ◽  
pp. 647
Author(s):  
Cedrique Fotsing ◽  
Nafissetou Nziengam ◽  
Christophe Bobda
Keyword(s):  
Execution Time ◽  
Point Cloud ◽  
Single Point ◽  
Point Clouds ◽  
Multiple Point ◽  
Global Alignment ◽  
Data Sets ◽  
Point Cloud Registration ◽  
Cloud Data ◽  
Local Plane

Point cloud registration combines multiple point cloud data sets collected from different positions using the same or different devices to form a single point cloud within a single coordinate system. Point cloud registration is usually achieved through spatial transformations that align and merge multiple point clouds into a single globally consistent model. In this paper, we present a new segmentation-based approach for point cloud registration. Our method consists of extracting plane structures from point clouds and then, using the 4-Point Congruent Sets (4PCS) technique, we estimate transformations that align the plane structures. Instead of a global alignment using all the points in the dataset, our method aligns 2-point clouds using their local plane structures. This considerably reduces the data size, computational workload, and execution time. Unlike conventional methods that seek to align the largest number of common points between entities, the new method aims to align the largest number of planes. Using partial point clouds of multiple real-world scenes, we demonstrate the superiority of our method compared to raw 4PCS in terms of quality of result (QoS) and execution time. Our method requires about half the execution time of 4PCS in all the tested datasets and produces better alignment of the point clouds.

scholarly journals Multi-Dimensional Underwater Point Cloud Detection Based on Deep Learning

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 884
Author(s):  
Chia-Ming Tsai ◽  
Yi-Horng Lai ◽  
Yung-Da Sun ◽  
Yu-Jen Chung ◽  
Jau-Woei Perng
Keyword(s):  
Deep Learning ◽  
Point Cloud ◽  
Three Dimensional ◽  
Point Clouds ◽  
Training Data ◽  
Network Architectures ◽  
Point Cloud Data ◽  
Data Types ◽  
Raw Data ◽  
Cloud Data

Numerous sensors can obtain images or point cloud data on land, however, the rapid attenuation of electromagnetic signals and the lack of light in water have been observed to restrict sensing functions. This study expands the utilization of two- and three-dimensional detection technologies in underwater applications to detect abandoned tires. A three-dimensional acoustic sensor, the BV5000, is used in this study to collect underwater point cloud data. Some pre-processing steps are proposed to remove noise and the seabed from raw data. Point clouds are then processed to obtain two data types: a 2D image and a 3D point cloud. Deep learning methods with different dimensions are used to train the models. In the two-dimensional method, the point cloud is transferred into a bird’s eye view image. The Faster R-CNN and YOLOv3 network architectures are used to detect tires. Meanwhile, in the three-dimensional method, the point cloud associated with a tire is cut out from the raw data and is used as training data. The PointNet and PointConv network architectures are then used for tire classification. The results show that both approaches provide good accuracy.

Ground filtering algorithm for mobile LIDAR using order and neighborhood point information

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Siyuan Huang ◽  
Limin Liu ◽  
Jian Dong ◽  
Xiongjun Fu ◽  
Leilei Jia
Keyword(s):  
Point Cloud ◽  
Point Clouds ◽  
Sorting Algorithm ◽  
Data Sets ◽  
Data Set ◽  
Spherical Coordinate ◽  
Content Type ◽  
Cloud Data ◽  
Filtering Algorithm ◽  
Field Environment

Purpose Most of the existing ground filtering algorithms are based on the Cartesian coordinate system, which is not compatible with the working principle of mobile light detection and ranging and difficult to obtain good filtering accuracy. The purpose of this paper is to improve the accuracy of ground filtering by making full use of the order information between the point and the point in the spherical coordinate. Design/methodology/approach First, the cloth simulation (CS) algorithm is modified into a sorting algorithm for scattered point clouds to obtain the adjacent relationship of the point clouds and to generate a matrix containing the adjacent information of the point cloud. Then, according to the adjacent information of the points, a projection distance comparison and local slope analysis are simultaneously performed. These results are integrated to process the point cloud details further and the algorithm is finally used to filter a point cloud in a scene from the KITTI data set. Findings The results show that the accuracy of KITTI point cloud sorting is 96.3% and the kappa coefficient of the ground filtering result is 0.7978. Compared with other algorithms applied to the same scene, the proposed algorithm has higher processing accuracy. Research limitations/implications Steps of the algorithm are parallel computing, which saves time owing to the small amount of computation. In addition, the generality of the algorithm is improved and it could be used for different data sets from urban streets. However, due to the lack of point clouds from the field environment with labeled ground points, the filtering result of this algorithm in the field environment needs further study. Originality/value In this study, the point cloud neighboring information was obtained by a modified CS algorithm. The ground filtering algorithm distinguish ground points and off-ground points according to the flatness, continuity and minimality of ground points in point cloud data. In addition, it has little effect on the algorithm results if thresholds were changed.

scholarly journals AUTOMATED BIM ENTITY RECONSTRUCTION FROM UNSTRUCTURED 3D POINTCLOUDS

2021 ◽  
Vol 30 ◽  
pp. 126-130
Author(s):  
Jan Voříšek ◽  
Bořek Patzák ◽  
Edita Dvořáková ◽  
Daniel Rypl
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Point Clouds ◽  
Human Interaction ◽  
Data Sets ◽  
Building Projects ◽  
Cloud Data ◽  
Data Points ◽  
Floor Plans ◽  
Large Building

Laser scanning is used widely in architecture and construction to document existing buildings by providing accurate data for creating a 3D model. The output is a set of data points in space, so-called point cloud. While point clouds can be directly rendered and inspected, they do not hold any semantics. Typically, engineers manually obtain floor plans, structural models, or the whole BIM model, which is a very time-consuming task for large building projects. In this contribution, we present the design and concept of a PointCloud2BIM library [1]. It provides a set of algorithms for automated or user assisted detection of fundamental entities from scanned point cloud data sets, such as floors, rooms, walls, and openings, and identification of the mutual relationships between them. The entity detection is based on a reasonable degree of human interaction (i.e., expected wall thickness). The results reside in a platform-agnostic JSON database allowing future integration into any existing BIM software.

scholarly journals Evaluation of Partially Overlapping 3D Point Cloud's Registration by using ICP variant and CloudCompare.

2014 ◽  
Vol XL-8 ◽  
pp. 891-897 ◽  
Author(s):  
Y. D. Rajendra ◽  
S. C. Mehrotra ◽  
K. V. Kale ◽  
R. R. Manza ◽  
R. K. Dhumal ◽  
...  
Keyword(s):  
Point Cloud ◽  
Point Clouds ◽  
Large Object ◽  
Point Cloud Registration ◽  
Icp Algorithm ◽  
Cloud Data ◽  
Registration Process ◽  
Heritage Site ◽  
Laser Scanners ◽  
Dense Point

Terrestrial Laser Scanners (TLS) are used to get dense point samples of large object’s surface. TLS is new and efficient method to digitize large object or scene. The collected point samples come into different formats and coordinates. Different scans are required to scan large object such as heritage site. Point cloud registration is considered as important task to bring different scans into whole 3D model in one coordinate system. Point clouds can be registered by using one of the three ways or combination of them, Target based, feature extraction, point cloud based. For the present study we have gone through Point Cloud Based registration approach. We have collected partially overlapped 3D Point Cloud data of Department of Computer Science & IT (DCSIT) building located in Dr. Babasaheb Ambedkar Marathwada University, Aurangabad. To get the complete point cloud information of the building we have taken 12 scans, 4 scans for exterior and 8 scans for interior façade data collection. There are various algorithms available in literature, but Iterative Closest Point (ICP) is most dominant algorithms. The various researchers have developed variants of ICP for better registration process. The ICP point cloud registration algorithm is based on the search of pairs of nearest points in a two adjacent scans and calculates the transformation parameters between them, it provides advantage that no artificial target is required for registration process. We studied and implemented three variants Brute Force, KDTree, Partial Matching of ICP algorithm in MATLAB. The result shows that the implemented version of ICP algorithm with its variants gives better result with speed and accuracy of registration as compared with CloudCompare Open Source software.

scholarly journals Dynamic Sensor Matching for Parallel Point Cloud Data Acquisition

2021 ◽  
Author(s):  
Simone Müller ◽  
Dieter Kranzlmüller
Keyword(s):  
Point Cloud ◽  
Dimensional Space ◽  
Three Dimensional ◽  
Object Perception ◽  
Point Clouds ◽  
Sensor Data ◽  
Virtual Object ◽  
Data Sets ◽  
Cloud Data ◽  
Digital Twins

Based on depth perception of individual stereo cameras, spatial structures can be derived as point clouds. The quality of such three-dimensional data is technically restricted by sensor limitations, latency of recording, and insufficient object reconstructions caused by surface illustration. Additionally external physical effects like lighting conditions, material properties, and reflections can lead to deviations between real and virtual object perception. Such physical influences can be seen in rendered point clouds as geometrical imaging errors on surfaces and edges. We propose the simultaneous use of multiple and dynamically arranged cameras. The increased information density leads to more details in surrounding detection and object illustration. During a pre-processing phase the collected data are merged and prepared. Subsequently, a logical analysis part examines and allocates the captured images to three-dimensional space. For this purpose, it is necessary to create a new metadata set consisting of image and localisation data. The post-processing reworks and matches the locally assigned images. As a result, the dynamic moving images become comparable so that a more accurate point cloud can be generated. For evaluation and better comparability we decided to use synthetically generated data sets. Our approach builds the foundation for dynamic and real-time based generation of digital twins with the aid of real sensor data.

scholarly journals Unsupervised Clustering of Multi-Perspective 3D Point Cloud Data in Marshes: A Case Study

2019 ◽  
Vol 11 (22) ◽  
pp. 2715 ◽  
Author(s):  
Chuyen Nguyen ◽  
Michael J. Starek ◽  
Philippe Tissot ◽  
James Gibeaut
Keyword(s):  
Land Cover ◽  
Point Cloud ◽  
Point Clouds ◽  
Unsupervised Clustering ◽  
Unmanned Aircraft ◽  
Data Sets ◽  
3D Point Cloud ◽  
Point Cloud Data ◽  
Clustering Method ◽  
Cloud Data

Dense three-dimensional (3D) point cloud data sets generated by Terrestrial Laser Scanning (TLS) and Unmanned Aircraft System based Structure-from-Motion (UAS-SfM) photogrammetry have different characteristics and provide different representations of the underlying land cover. While there are differences, a common challenge associated with these technologies is how to best take advantage of these large data sets, often several hundred million points, to efficiently extract relevant information. Given their size and complexity, the data sets cannot be efficiently and consistently separated into homogeneous features without the use of automated segmentation algorithms. This research aims to evaluate the performance and generalizability of an unsupervised clustering method, originally developed for segmentation of TLS point cloud data in marshes, by extending it to UAS-SfM point clouds. The combination of two sets of features are extracted from both datasets: “core” features that can be extracted from any 3D point cloud and “sensor specific” features unique to the imaging modality. Comparisons of segmented results based on producer’s and user’s accuracies allow for identifying the advantages and limitations of each dataset and determining the generalization of the clustering method. The producer’s accuracies suggest that UAS-SfM (94.7%) better represents tidal flats, while TLS (99.5%) is slightly more suitable for vegetated areas. The users’ accuracies suggest that UAS-SfM outperforms TLS in vegetated areas with 98.6% of those points identified as vegetation actually falling in vegetated areas whereas TLS outperforms UAS-SfM in tidal flat areas with 99.2% user accuracy. Results demonstrate that the clustering method initially developed for TLS point cloud data transfers well to UAS-SfM point cloud data to enable consistent and accurate segmentation of marsh land cover via an unsupervised method.

Robust Initial Matching of Free-Form Objects Represented by Point Clouds

2012 ◽  
Vol 134 (2) ◽  
Author(s):  
Daoshan OuYang ◽  
Hsi-Yung Feng ◽  
Nimun A. Jahangir ◽  
Hao Song
Keyword(s):  
Point Cloud ◽  
Distance Measure ◽  
Point Clouds ◽  
Matching Condition ◽  
Surface Shape ◽  
Free Form ◽  
Data Sets ◽  
Point Cloud Data ◽  
Cloud Data ◽  
Geometric Inspection

The problem of best matching two point cloud data sets or, mathematically, identifying the best rigid-body transformation matrix between them, arises in many application areas such as geometric inspection and object recognition. Traditional methods establish the correspondence between the two data sets via the measure of shortest Euclidean distance and rely on an iterative procedure to converge to the solution. The effectiveness of such methods is highly dependent on the initial condition for the numerical iteration. This paper proposes a new robust scheme to automatically generate the needed initial matching condition. The initial matching scheme undertakes the alignment in a global manner and yields a rough match of the data sets. Instead of directly minimizing the distance measure between the data sets, the focus of the initial matching is on the alignment of shape features. This is achieved by evaluating Delaunay pole spheres for the point cloud data sets and analyzing their distributions to map out the intrinsic features of the underlying surface shape. The initial matching result is then fine-tuned by the final matching step via the traditional iterative closest point method. Case studies have been performed to validate the effectiveness of the proposed initial matching scheme.

scholarly journals ECPC-ICP: A 6D Vehicle Pose Estimation Method by Fusing the Roadside Lidar Point Cloud and Road Feature

Sensors ◽  
2021 ◽  
Vol 21 (10) ◽  
pp. 3489
Author(s):  
Bo Gu ◽  
Jianxun Liu ◽  
Huiyuan Xiong ◽  
Tongtong Li ◽  
Yuelong Pan
Keyword(s):  
Pose Estimation ◽  
Point Cloud ◽  
Estimation Method ◽  
Principal Component ◽  
Point Clouds ◽  
Target Point ◽  
Test Environment ◽  
Cloud Data ◽  
Practical Applications ◽  
The Road

In the vehicle pose estimation task based on roadside Lidar in cooperative perception, the measurement distance, angle, and laser resolution directly affect the quality of the target point cloud. For incomplete and sparse point clouds, current methods are either less accurate in correspondences solved by local descriptors or not robust enough due to the reduction of effective boundary points. In response to the above weakness, this paper proposed a registration algorithm Environment Constraint Principal Component-Iterative Closest Point (ECPC-ICP), which integrated road information constraints. The road normal feature was extracted, and the principal component of the vehicle point cloud matrix under the road normal constraint was calculated as the initial pose result. Then, an accurate 6D pose was obtained through point-to-point ICP registration. According to the measurement characteristics of the roadside Lidars, this paper defined the point cloud sparseness description. The existing algorithms were tested on point cloud data with different sparseness. The simulated experimental results showed that the positioning MAE of ECPC-ICP was about 0.5% of the vehicle scale, the orientation MAE was about 0.26°, and the average registration success rate was 95.5%, which demonstrated an improvement in accuracy and robustness compared with current methods. In the real test environment, the positioning MAE was about 2.6% of the vehicle scale, and the average time cost was 53.19 ms, proving the accuracy and effectiveness of ECPC-ICP in practical applications.

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.

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