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12 pages, 1551 KiB

Open AccessFeature PaperArticle

Multiscale Supervised Classification of Point Clouds with Urban and Forest Applications

by Carlos Cabo, Celestino Ordóñez, Fernando Sáchez-Lasheras, Javier Roca-Pardiñas and Javier de Cos-Juez

Sensors 2019, 19(20), 4523; https://doi.org/10.3390/s19204523 - 17 Oct 2019

Cited by 11 | Viewed by 2347

Abstract

We analyze the utility of multiscale supervised classification algorithms for object detection and extraction from laser scanning or photogrammetric point clouds. Only the geometric information (the point coordinates) was considered, thus making the method independent of the systems used to collect the data. [...] Read more.

We analyze the utility of multiscale supervised classification algorithms for object detection and extraction from laser scanning or photogrammetric point clouds. Only the geometric information (the point coordinates) was considered, thus making the method independent of the systems used to collect the data. A maximum of five features (input variables) was used, four of them related to the eigenvalues obtained from a principal component analysis (PCA). PCA was carried out at six scales, defined by the diameter of a sphere around each observation. Four multiclass supervised classification models were tested (linear discriminant analysis, logistic regression, support vector machines, and random forest) in two different scenarios, urban and forest, formed by artificial and natural objects, respectively. The results obtained were accurate (overall accuracy over 80% for the urban dataset, and over 93% for the forest dataset), in the range of the best results found in the literature, regardless of the classification method. For both datasets, the random forest algorithm provided the best solution/results when discrimination capacity, computing time, and the ability to estimate the relative importance of each variable are considered together. Full article

(This article belongs to the Special Issue Advances in Laser Scanning and Photogrammetry for Sustainable Development)

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17 pages, 3440 KiB

Open AccessArticle

Real-Time Efficient Relocation Algorithm Based on Depth Map for Small-Range Textureless 3D Scanning

by Fengbo Zhu, Shunyi Zheng, Xiaonan Wang, Yuan He, Li Gui and Liangxiong Gong

Sensors 2019, 19(18), 3855; https://doi.org/10.3390/s19183855 - 6 Sep 2019

Cited by 2 | Viewed by 2799

Abstract

As an important part of industrial 3D scanning, a relocation algorithm is used to restore the position and the pose of a 3D scanner or to perform closed-loop detection. The real time and the relocation correct ratio are prominent and difficult points in [...] Read more.

As an important part of industrial 3D scanning, a relocation algorithm is used to restore the position and the pose of a 3D scanner or to perform closed-loop detection. The real time and the relocation correct ratio are prominent and difficult points in 3D scanning relocation research. By utilizing the depth map information captured by a binocular vision 3D scanner, we developed an efficient and real-time relocation algorithm to estimate the current position and pose of the sensor real-time and high-correct-rate relocation algorithm for small-range 3D texture less scanning. This algorithm mainly involves feature calculation, feature database construction and query, feature matching verification, and rigid transformation calculation; through the four parts, the initial position and pose of the sensors in the global coordinate system is obtained. In the experiments, the efficiency and the correct-rate of the proposed relocation algorithm were elaborately verified by offline and online experiments on four objects of different sizes, and a smooth and a rough surface. With more data frames and feature points, the relocation could be maintained real time within 200 ms, and a high correct rate of more than 90% could be realized. The experimental results showed that the proposed algorithm could achieve a real-time and high-correct-ratio relocation. Full article

(This article belongs to the Special Issue Advances in Laser Scanning and Photogrammetry for Sustainable Development)

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29 pages, 11040 KiB

Open AccessArticle

New Target for Accurate Terrestrial Laser Scanning and Unmanned Aerial Vehicle Point Cloud Registration

by Tilen Urbančič, Žiga Roškar, Mojca Kosmatin Fras and Dejan Grigillo

Sensors 2019, 19(14), 3179; https://doi.org/10.3390/s19143179 - 19 Jul 2019

Cited by 13 | Viewed by 5034

Abstract

The main goal of our research was to design and implement an innovative target that would be suitable for accurately registering point clouds produced from unmanned aerial vehicle (UAV) images and terrestrial laser scans. Our new target is composed of three perpendicular planes [...] Read more.

The main goal of our research was to design and implement an innovative target that would be suitable for accurately registering point clouds produced from unmanned aerial vehicle (UAV) images and terrestrial laser scans. Our new target is composed of three perpendicular planes that combine the properties of plane and volume targets. The new target enables the precise determination of reference target points in aerial and terrestrial point clouds. Different types of commonly used plane and volume targets as well as the new target were placed in an established test area in order to evaluate their performance. The targets were scanned from multiple scanner stations and surveyed with an unmanned aerial vehicle DJI Phantom 4 PRO at three different altitudes (20, 40, and 75 m). The reference data were measured with a Leica Nova MS50 MultiStation. Several registrations were performed, each time with a different target. The quality of these registrations was assessed on the check points. The results showed that the new target yielded the best results in all cases, which confirmed our initial expectations. The proposed new target is innovative and not difficult to create and use. Full article

(This article belongs to the Special Issue Advances in Laser Scanning and Photogrammetry for Sustainable Development)

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18 pages, 8528 KiB

Open AccessArticle

Customized Mobile LiDAR System for Manhole Cover Detection and Identification

by Zhanying Wei, Mengmeng Yang, Liuzhao Wang, Hao Ma, Xuexia Chen and Ruofei Zhong

Sensors 2019, 19(10), 2422; https://doi.org/10.3390/s19102422 - 27 May 2019

Cited by 24 | Viewed by 6401

Abstract

Manhole covers, which are a key element of urban infrastructure management, have a direct impact on travel safety. At present, there is no automatic, safe, and efficient system specially used for the intelligent detection, identification, and assessment of manhole covers. In this work, [...] Read more.

Manhole covers, which are a key element of urban infrastructure management, have a direct impact on travel safety. At present, there is no automatic, safe, and efficient system specially used for the intelligent detection, identification, and assessment of manhole covers. In this work, we developed an automatic detection, identification, and assessment system for manhole covers. First, we developed a sequential exposure system via the addition of multiple cameras in a symmetrical arrangement to realize the joint acquisition of high-precision laser data and ultra-high-resolution ground images. Second, we proposed an improved histogram of an oriented gradient with symmetry features and a support vector machine method to detect manhole covers effectively and accurately, by using the intensity images and ground orthophotos that are derived from the laser points and images, respectively, and apply the graph segmentation and statistical analysis to achieve the detection, identification, and assessment of manhole covers. Qualitative and quantitative analyses are performed using large experimental datasets that were acquired with the modified manhole-cover detection system. The detected results yield an average accuracy of 96.18%, completeness of 94.27%, and F-measure value of 95.22% in manhole cover detection. Defective manhole-cover monitoring and manhole-cover ownership information are achieved from these detection results. The results not only provide strong support for road administration works, such as data acquisition, manhole cover inquiry and inspection, and statistical analysis of resources, but also demonstrate the feasibility and effectiveness of the proposed method, which reduces the risk involved in performing manual inspections, improves the manhole-cover detection accuracy, and serves as a powerful tool in intelligent road administration. Full article

(This article belongs to the Special Issue Advances in Laser Scanning and Photogrammetry for Sustainable Development)

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16 pages, 3664 KiB

Open AccessArticle

A 10-nm Spectral Resolution Hyperspectral LiDAR System Based on an Acousto-Optic Tunable Filter

by Yuwei Chen, Wei Li, Juha Hyyppä, Ning Wang, Changhui Jiang, Fanrong Meng, Lingli Tang, Eetu Puttonen and Chuanrong Li

Sensors 2019, 19(7), 1620; https://doi.org/10.3390/s19071620 - 4 Apr 2019

Cited by 51 | Viewed by 6851

Abstract

Hyperspectral LiDAR (HSL) technology can obtain spectral and ranging information from targets by processing the recorded waveforms and measuring the time of flight (ToF). With the development of the supercontinuum laser (SCL), it is technically easier to develop an active hyperspectral LiDAR system [...] Read more.

Hyperspectral LiDAR (HSL) technology can obtain spectral and ranging information from targets by processing the recorded waveforms and measuring the time of flight (ToF). With the development of the supercontinuum laser (SCL), it is technically easier to develop an active hyperspectral LiDAR system that can simultaneously collect both spatial information and extensive spectral information from targets. Compared with traditional LiDAR technology, which can only obtain range and intensity information at the selected spectral wavelengths, HSL detection technology has demonstrated its potential and adaptability for various quantitative applications from its spectrally resolved waveforms. However, with most previous HSLs, the collected spectral information is discrete, and such information might be insufficient and restrict the further applicability of the HSLs. In this paper, a tunable HSL technology using an acousto-optic tunable filter (AOTF) as a spectroscopic device was proposed, designed, and tested to address this issue. Both the general range precision and the accuracy of the spectral measurement were evaluated. By tuning the spectroscopic device in the time dimension, the proposed AOTF-HSL could achieve backscattered echo with continuous coverage of the full spectrum of 500–1000 nm, which had the unique characteristics of a continuous spectrum in the visible and near infrared (VNIR) regions with 10 nm spectral resolution. Yellow and green leaves from four plants (aloe, dracaena, balata, and radermachera) were measured using the AOTF-HSL to assess its feasibility in agriculture application. The spectral profiles measured by a standard spectrometer (SVC^© HR-1024) were used as a reference for evaluating the measurements of the AOTF-HSL. The difference between the spectral measurements collected from active and passive instruments was minor. The comparison results show that the AOTF-based consecutive and high spectral resolution HSL was effective for this application. Full article

(This article belongs to the Special Issue Advances in Laser Scanning and Photogrammetry for Sustainable Development)

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14 pages, 3525 KiB

Open AccessArticle

Gear Tooth Profile Reconstruction via Geometrically Compensated Laser Triangulation Measurements

by Hao Tian, Fan Wu and Yongjun Gong

Sensors 2019, 19(7), 1589; https://doi.org/10.3390/s19071589 - 2 Apr 2019

Cited by 11 | Viewed by 5321

Abstract

Precision modeling of the hydraulic gear pump pressure dynamics depends on the accurate prediction of volumetric displacement in the inter-tooth spaces of the gear. By accurate reconstruction of the gear profile, detailed transient volumetric information can be determined. Therefore, this paper reports a [...] Read more.

Precision modeling of the hydraulic gear pump pressure dynamics depends on the accurate prediction of volumetric displacement in the inter-tooth spaces of the gear. By accurate reconstruction of the gear profile, detailed transient volumetric information can be determined. Therefore, this paper reports a non-contact gear measurement device using two opposing laser triangulation sensors, and the key geometrical models to reconstruct the profile with geometrical error compensation. An optimization-based key parameter calculation method is also proposed to find the unknown orientation of the sensor. Finally, an experimental setup is established, the performance of the device is tested and the geometric model is validated. Initial results showed that the method is able to reconstruct the target tooth profile and compensated results can reduce the geometrical error by up to 98% compared to the uncalibrated ones. Full article

(This article belongs to the Special Issue Advances in Laser Scanning and Photogrammetry for Sustainable Development)

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17 pages, 5401 KiB

Open AccessArticle

Robust Normal Estimation for 3D LiDAR Point Clouds in Urban Environments

by Ruibin Zhao, Mingyong Pang, Caixia Liu and Yanling Zhang

Sensors 2019, 19(5), 1248; https://doi.org/10.3390/s19051248 - 12 Mar 2019

Cited by 23 | Viewed by 5765

Abstract

Normal estimation is a crucial first step for numerous light detection and ranging (LiDAR) data-processing algorithms, from building reconstruction, road extraction, and ground-cover classification to scene rendering. For LiDAR point clouds in urban environments, this paper presents a robust method to estimate normals [...] Read more.

Normal estimation is a crucial first step for numerous light detection and ranging (LiDAR) data-processing algorithms, from building reconstruction, road extraction, and ground-cover classification to scene rendering. For LiDAR point clouds in urban environments, this paper presents a robust method to estimate normals by constructing an octree-based hierarchical representation for the data and detecting a group of large enough consistent neighborhoods at multiscales. Consistent neighborhoods are mainly determined based on the observation that an urban environment is typically comprised of regular objects, e.g., buildings, roads, and the ground surface, and irregular objects, e.g., trees and shrubs; the surfaces of most regular objects can be approximatively represented by a group of local planes. Even in the frequent presence of heavy noise and anisotropic point samplings in LiDAR data, our method is capable of estimating robust normals for kinds of objects in urban environments, and the estimated normals are beneficial to more accurately segment and identify the objects, as well as preserving their sharp features and complete outlines. The proposed method was experimentally validated both on synthetic and real urban LiDAR datasets, and was compared to state-of-the-art methods. Full article

(This article belongs to the Special Issue Advances in Laser Scanning and Photogrammetry for Sustainable Development)

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17 pages, 6609 KiB

Open AccessArticle

Automatic Registration of Optical Images with Airborne LiDAR Point Cloud in Urban Scenes Based on Line-Point Similarity Invariant and Extended Collinearity Equations

by Shubiao Peng, Hongchao Ma and Liang Zhang

Sensors 2019, 19(5), 1086; https://doi.org/10.3390/s19051086 - 3 Mar 2019

Cited by 17 | Viewed by 4684

Abstract

This paper proposes a novel method to achieve the automatic registration of optical images and Light Detection and Ranging (LiDAR) points in urban areas. The whole procedure, which adopts a coarse-to-precise registration strategy, can be summarized as follows: Coarse registration is performed through [...] Read more.

This paper proposes a novel method to achieve the automatic registration of optical images and Light Detection and Ranging (LiDAR) points in urban areas. The whole procedure, which adopts a coarse-to-precise registration strategy, can be summarized as follows: Coarse registration is performed through a conventional point-feature-based method. The points needed can be extracted from both datasets through a matured point extractor, such as the Forster operator, followed by the extraction of straight lines. Considering that lines are mainly from building roof edges in urban scenes, and being aware of their inaccuracy when extracted from an irregularly spaced point cloud, an “infinitesimal feature analysis method” fully utilizing LiDAR scanning characteristics is proposed to refine edge lines. Points which are matched between the image and LiDAR data are then applied as guidance to search for matched lines via the line-point similarity invariant. Finally, a transformation function based on extended collinearity equations is applied to achieve precise registration. The experimental results show that the proposed method outperforms the conventional ones in terms of the registration accuracy and automation level. Full article

(This article belongs to the Special Issue Advances in Laser Scanning and Photogrammetry for Sustainable Development)

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16 pages, 5296 KiB

Open AccessArticle

A Multi-View Stereo Measurement System Based on a Laser Scanner for Fine Workpieces

by Limei Song, Siyuan Sun, Yangang Yang, Xinjun Zhu, Qinghua Guo and Huaidong Yang

Sensors 2019, 19(2), 381; https://doi.org/10.3390/s19020381 - 18 Jan 2019

Cited by 30 | Viewed by 5238

Abstract

A new solution to the high-quality 3D reverse modeling problem of complex surfaces for fine workpieces is presented using a laser line-scanning sensor. Due to registration errors, measurement errors, deformations, etc., a fast and accurate method is important in machine vision measurement. This [...] Read more.

A new solution to the high-quality 3D reverse modeling problem of complex surfaces for fine workpieces is presented using a laser line-scanning sensor. Due to registration errors, measurement errors, deformations, etc., a fast and accurate method is important in machine vision measurement. This paper builds a convenient and economic multi-view stereo (MVS) measurement system based on a linear stage and a rotary stage to reconstruct the measured object surface completely and accurately. In the proposed technique, the linear stage is used to generate the trigger signal and synchronize the laser sensor scanning; the rotary stage is used to rotate the object and obtain multi-view point cloud data, and then the multi-view point cloud data are registered and integrated into a 3D model. The measurement results show a measurement accuracy of 0.075 mm for a 360° reconstruction in 34 s, and some evaluation experiments were carried out to demonstrate the validity and practicability of the proposed technique. Full article

(This article belongs to the Special Issue Advances in Laser Scanning and Photogrammetry for Sustainable Development)

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