Rapid registration method by using partial 3D point clouds

Landslides are among the major threats to urban landscape and manmade infrastructure. They often cause economic losses, property damages, and loss of lives. Temporal monitoring data of landslides from different epochs empowers the evaluation of landslide progression. Alignment of overlapping surfaces from two or more epochs is crucial for the proper analysis of landslide dynamics. The traditional methods for point-cloud-based landslide monitoring rely on using a variation of the Iterative Closest Point (ICP) registration procedure to align any reconstructed surfaces from different epochs to a common reference frame. However, sometimes the ICP-based registration can fail or may not provide sufficient accuracy. For example, point clouds from different epochs might fit to local minima due to lack of geometrical variability within the data. Also, manual interaction is required to exclude any non-stable areas from the registration process. In this paper, a robust image-based registration method is introduced for the simultaneous evaluation of all registration parameters. This includes the Interior Orientation Parameters (IOPs) of the camera and the Exterior Orientation Parameters (EOPs) of the involved images from all available observation epochs via a bundle block adjustment with self-calibration. Next, a semi-global dense matching technique is implemented to generate dense 3D point clouds for each epoch using the images captured in a particular epoch separately. The normal distances between any two consecutive point clouds can then be readily computed, because the point clouds are already effectively co-registered. A low-cost DJI Phantom II Unmanned Aerial Vehicle (UAV) was customised and used in this research for temporal data collection over an active soil creep area in Lethbridge, Alberta, Canada. The customisation included adding a GPS logger and a Large-Field-Of-View (LFOV) action camera which facilitated capturing high-resolution geo-tagged images in two epochs over the period of one year (i.e., May 2014 and May 2015). Note that due to the coarse accuracy of the on-board GPS receiver (e.g., +/- 5-10 m) the geo-tagged positions of the images were only used as initial values in the bundle block adjustment. Normal distances, signifying detected changes, varying from 20 cm to 4 m were identified between the two epochs. The accuracy of the co-registered surfaces was estimated by comparing non-active patches within the monitored area of interest. Since these non-active sub-areas are stationary, the computed normal distances should theoretically be close to zero. The quality control of the registration results showed that the average normal distance was approximately 4 cm, which is within the noise level of the reconstructed surfaces.

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An Efficient Probabilistic Registration Based on Shape Descriptor for Heritage Field Inspection

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi9120759 ◽

2020 ◽

Vol 9 (12) ◽

pp. 759

Author(s):

Yufu Zang ◽

Bijun Li ◽

Xiongwu Xiao ◽

Jianfeng Zhu ◽

Fancong Meng

Keyword(s):

Cultural Heritage ◽

Solution Space ◽

Point Clouds ◽

Shape Descriptor ◽

Free Form ◽

Registration Method ◽

Field Inspection ◽

3D Point Clouds ◽

Quality Issues ◽

Cultural Heritage Objects

Heritage documentation is implemented by digitally recording historical artifacts for the conservation and protection of these cultural heritage objects. As efficient spatial data acquisition tools, laser scanners have been widely used to collect highly accurate three-dimensional (3D) point clouds without damaging the original structure and the environment. To ensure the integrity and quality of the collected data, field inspection (i.e., on-spot checking the data quality) should be carried out to determine the need for additional measurements (i.e., extra laser scanning for areas with quality issues such as data missing and quality degradation). To facilitate inspection of all collected point clouds, especially checking the quality issues in overlaps between adjacent scans, all scans should be registered together. Thus, a point cloud registration method that is able to register scans fast and robustly is required. To fulfill the aim, this study proposes an efficient probabilistic registration for free-form cultural heritage objects by integrating the proposed principal direction descriptor and curve constraints. We developed a novel shape descriptor based on a local frame of principal directions. Within the frame, its density and distance feature images were generated to describe the shape of the local surface. We then embedded the descriptor into a probabilistic framework to reject ambiguous matches. Spatial curves were integrated as constraints to delimit the solution space. Finally, a multi-view registration was used to refine the position and orientation of each scan for the field inspection. Comprehensive experiments show that the proposed method was able to perform well in terms of rotation error, translation error, robustness, and runtime and outperformed some commonly used approaches.

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A ROBUST REGISTRATION ALGORITHM FOR POINT CLOUDS FROM UAV IMAGES FOR CHANGE DETECTION

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

10.5194/isprs-archives-xli-b1-765-2016 ◽

2016 ◽

Vol XLI-B1 ◽

pp. 765-772 ◽

Cited By ~ 2

Author(s):

A. Al-Rawabdeh ◽

H. Al-Gurrani ◽

K. Al-Durgham ◽

I. Detchev ◽

F. He ◽

...

Keyword(s):

Point Clouds ◽

Large Field ◽

Economic Losses ◽

Landslide Monitoring ◽

Registration Method ◽

Bundle Block Adjustment ◽

Area Of Interest ◽

3D Point Clouds ◽

Reconstructed Surfaces ◽

Orientation Parameters

Landslides are among the major threats to urban landscape and manmade infrastructure. They often cause economic losses, property damages, and loss of lives. Temporal monitoring data of landslides from different epochs empowers the evaluation of landslide progression. Alignment of overlapping surfaces from two or more epochs is crucial for the proper analysis of landslide dynamics. The traditional methods for point-cloud-based landslide monitoring rely on using a variation of the Iterative Closest Point (ICP) registration procedure to align any reconstructed surfaces from different epochs to a common reference frame. However, sometimes the ICP-based registration can fail or may not provide sufficient accuracy. For example, point clouds from different epochs might fit to local minima due to lack of geometrical variability within the data. Also, manual interaction is required to exclude any non-stable areas from the registration process. In this paper, a robust image-based registration method is introduced for the simultaneous evaluation of all registration parameters. This includes the Interior Orientation Parameters (IOPs) of the camera and the Exterior Orientation Parameters (EOPs) of the involved images from all available observation epochs via a bundle block adjustment with self-calibration. Next, a semi-global dense matching technique is implemented to generate dense 3D point clouds for each epoch using the images captured in a particular epoch separately. The normal distances between any two consecutive point clouds can then be readily computed, because the point clouds are already effectively co-registered. A low-cost DJI Phantom II Unmanned Aerial Vehicle (UAV) was customised and used in this research for temporal data collection over an active soil creep area in Lethbridge, Alberta, Canada. The customisation included adding a GPS logger and a Large-Field-Of-View (LFOV) action camera which facilitated capturing high-resolution geo-tagged images in two epochs over the period of one year (i.e., May 2014 and May 2015). Note that due to the coarse accuracy of the on-board GPS receiver (e.g., +/- 5-10 m) the geo-tagged positions of the images were only used as initial values in the bundle block adjustment. Normal distances, signifying detected changes, varying from 20 cm to 4 m were identified between the two epochs. The accuracy of the co-registered surfaces was estimated by comparing non-active patches within the monitored area of interest. Since these non-active sub-areas are stationary, the computed normal distances should theoretically be close to zero. The quality control of the registration results showed that the average normal distance was approximately 4 cm, which is within the noise level of the reconstructed surfaces.

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A novel scan registration method based on the feature-less global descriptor – spherical entropy image

Industrial Robot the international journal of robotics research and application ◽

10.1108/ir-11-2016-0329 ◽

2017 ◽

Vol 44 (4) ◽

pp. 552-563 ◽

Cited By ~ 1

Author(s):

Bo Sun ◽

Yadan Zeng ◽

Houde Dai ◽

Junhao Xiao ◽

Jianwei Zhang

Keyword(s):

Mobile Robotics ◽

High Reliability ◽

Three Dimensional ◽

Point Clouds ◽

Registration Method ◽

Content Type ◽

Spherical Fourier Transform ◽

3D Point Clouds ◽

3D Data Processing ◽

Scan Registration

Purpose This paper aims to present the spherical entropy image (SEI), a novel global descriptor for the scan registration of three-dimensional (3D) point clouds. This paper also introduces a global feature-less scan registration strategy based on SEI. It is advantageous for 3D data processing in the scenarios such as mobile robotics and reverse engineering. Design/methodology/approach The descriptor works through representing the scan by a spherical function named SEI, whose properties allow to decompose the six-dimensional transformation into 3D rotation and 3D translation. The 3D rotation is estimated by the generalized convolution theorem based on the spherical Fourier transform of SEI. Then, the translation recovery is determined by phase only matched filtering. Findings No explicit features and planar segments should be contained in the input data of the method. The experimental results illustrate the parameter independence, high reliability and efficiency of the novel algorithm in registration of feature-less scans. Originality/value A novel global descriptor (SEI) for the scan registration of 3D point clouds is presented. It inherits both descriptive power of signature-based methods and robustness of histogram-based methods. A high reliability and efficiency registration method of scans based on SEI is also demonstrated.

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An Accurate TLS and UAV Image Point Clouds Registration Method for Deformation Detection of Chaotic Hillside Areas

Remote Sensing ◽

10.3390/rs11060647 ◽

2019 ◽

Vol 11 (6) ◽

pp. 647 ◽

Cited By ~ 10

Author(s):

Yufu Zang ◽

Bisheng Yang ◽

Jianping Li ◽

Haiyan Guan

Keyword(s):

Laser Scanning ◽

Detection Method ◽

Three Dimensional ◽

Point Clouds ◽

Image Point ◽

Numerical Representation ◽

Registration Method ◽

3D Point Clouds ◽

Multi Temporal ◽

Difficult Terrain

Deformation detection determines the quantified change of a scene’s geometric state, which is of great importance for the mitigation of hazards and property loss from earth observation. Terrestrial laser scanning (TLS) provides an efficient and flexible solution to rapidly capture high precision three-dimensional (3D) point clouds of hillside areas. Most existing methods apply multi-temporal TLS surveys to detect deformations depending on a variety of ground control points (GCPs). However, on the one hand, the deployment of various GCPs is time-consuming and labor-intensive, particularly for difficult terrain areas. On the other hand, in most cases, TLS stations do not form a closed loop, such that cumulative errors cannot be corrected effectively by the existing methods. To overcome these drawbacks, this paper proposes a deformation detection method with limited GCPs based on a novel registration algorithm that accurately registers TLS stations to the UAV (Unmanned Aerial Vehicle) dense image points. First, the proposed method extracts patch primitives from smoothed hillside points, and adjacent TLS scans are pairwise registered by comparing the geometric and topological information of or between patches. Second, a new multi-station adjustment algorithm is proposed, which makes full use of locally closed loops to reach the global optimal registration. Finally, digital elevation models (DEMs, a DEM is a numerical representation of the terrain surface, formed by height points to represent the topography), slope and aspect maps, and vertical sections are generated from multi-temporal TLS surveys to detect and analyze the deformations. Comprehensive experiments demonstrate that the proposed deformation detection method obtains good performance for the hillside areas with limited (few) GCPs.

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AN UNSUPERVISED REGISTRATION OF 3D POINT CLOUDS TO 2D CAD MODEL: A CASE STUDY OF FLOOR PLAN

ISPRS Annals of Photogrammetry Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-annals-v-2-2020-9-2020 ◽

2020 ◽

Vol V-2-2020 ◽

pp. 9-13

Author(s):

A. Alizadeh Naeini ◽

A. Ahmad ◽

M. M. Sheikholeslami ◽

P. Claudio ◽

G. Sohn

Keyword(s):

Computer Aided Design ◽

Point Clouds ◽

Easy Access ◽

Cad Model ◽

Registration Method ◽

Binary Images ◽

3D Point Clouds ◽

Cad Models ◽

Manual Registration ◽

Aided Design

Abstract. Thanks to the proliferation of commodity 3D devices such as HoloLens, one can have easy access to the 3D model of indoor building objects. However, this model does not match 2D available computer-aided design (CAD) models as the as-built model. To address this problem, in this study, a 3-step registration method is proposed. First, binary images, including walls and background, are generated for the 3D point cloud (PC) and the 2D CAD model. Then, 2D-to-2D corresponding pixels (CPs) are extracted based on the intersection of walls in each binary image of PC (BIPC) and binary CAD (BCAD) model. Since the 3D PC space coordinates (XYZ) of all BIPC's pixels are known, BIPC part of the 2D-to-2D CPs can be considered 3D. Lastly, the parameters of the 8-parameter affine are estimated using the 2D-to-3D CPs, which are pixel coordinates in BCAD model as well as their correspondences in the 3D PC space. Experimental results indicate the efficiency of our proposed method compared to manual registration.

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Omnidirectional 3D Point Clouds Using Dual Kinect Sensors

Journal of Sensors ◽

10.1155/2019/6295956 ◽

2019 ◽

Vol 2019 ◽

pp. 1-17 ◽

Cited By ~ 4

Author(s):

Seokmin Yun ◽

Jaewon Choi ◽

Chee Sun Won

Keyword(s):

Human Body ◽

Point Clouds ◽

Registration Method ◽

Icp Algorithm ◽

Kinect V2 ◽

3D Point Clouds ◽

3D Data ◽

Portable Camera ◽

Calibration Parameters ◽

Checker Board

This paper proposes a registration method for two sets of point clouds obtained from dual Kinect V2 sensors, which are facing each other to capture omnidirectional 3D data of the objects located in between the two sensors. Our approach aims at achieving a handy registration without the calibration-assisting devices such as the checker board. Therefore, it is suitable in portable camera setting environments with frequent relocations. The basic idea of the proposed registration method is to exploit the skeleton information of the human body provided by the two Kinect V2 sensors. That is, a set of correspondence pairs in skeleton joints of human body detected by Kinect V2 sensors is used to determine the calibration matrices, then Iterative Closest Point (ICP) algorithm is adopted for finely tuning the calibration parameters. The performance of the proposed method is evaluated by constructing 3D point clouds for human bodies and by making geometric measurements for cylindrical testing objects.

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A Registration Method for 3D Point Clouds with Convolutional Neural Network

Intelligent Robotics and Applications - Lecture Notes in Computer Science ◽

10.1007/978-3-319-65298-6_35 ◽

2017 ◽

pp. 377-387 ◽

Cited By ~ 1

Author(s):

Shangyou Ai ◽

Lei Jia ◽

Chungang Zhuang ◽

Han Ding

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Point Clouds ◽

Registration Method ◽

3D Point Clouds

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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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