Evaluation of a New Point Clouds Registration Method Based on Group Averaging Features

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.

Download Full-text

SPPD: A Novel Reassembly Method for 3D Terracotta Warrior Fragments Based on Fracture Surface Information

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi10080525 ◽

2021 ◽

Vol 10 (8) ◽

pp. 525

Author(s):

Wenmin Yao ◽

Tong Chu ◽

Wenlong Tang ◽

Jingyu Wang ◽

Xin Cao ◽

...

Keyword(s):

Fracture Surface ◽

Principal Component ◽

Point Clouds ◽

Three Dimension ◽

Registration Method ◽

Series Of Experiments ◽

Coarse To Fine ◽

Fine Registration ◽

Common Situation ◽

Better Than

As one of China′s most precious cultural relics, the excavation and protection of the Terracotta Warriors pose significant challenges to archaeologists. A fairly common situation in the excavation is that the Terracotta Warriors are mostly found in the form of fragments, and manual reassembly among numerous fragments is laborious and time-consuming. This work presents a fracture-surface-based reassembling method, which is composed of SiamesePointNet, principal component analysis (PCA), and deep closest point (DCP), and is named SPPD. Firstly, SiamesePointNet is proposed to determine whether a pair of point clouds of 3D Terracotta Warrior fragments can be reassembled. Then, a coarse-to-fine registration method based on PCA and DCP is proposed to register the two fragments into a reassembled one. The above two steps iterate until the termination condition is met. A series of experiments on real-world examples are conducted, and the results demonstrate that the proposed method performs better than the conventional reassembling methods. We hope this work can provide a valuable tool for the virtual restoration of three-dimension cultural heritage artifacts.

Download Full-text

Automatic 3D point clouds registration method

10.1117/12.871687 ◽

2010 ◽

Cited By ~ 3

Author(s):

Ting Wu ◽

Naiguang Lv ◽

Xiaoping Lou ◽

Peng Sun

Keyword(s):

Point Clouds ◽

Registration Method ◽

3D Point Clouds

Download Full-text

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/isprsarchives-xli-b1-765-2016 ◽

2016 ◽

Vol XLI-B1 ◽

pp. 765-772 ◽

Cited By ~ 5

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.

Download Full-text

An ICP-Based Point Clouds Registration Method for Indoor Environment Modeling

Lecture Notes in Electrical Engineering - Advanced Multimedia and Ubiquitous Engineering ◽

10.1007/978-981-32-9244-4_48 ◽

2019 ◽

pp. 339-344

Author(s):

Su Sun ◽

Wei Song ◽

Yifei Tian ◽

Simon Fong

Keyword(s):

Indoor Environment ◽

Point Clouds ◽

Environment Modeling ◽

Registration Method

Download Full-text

A Robust Registration Method for Autonomous Driving Pose Estimation in Urban Dynamic Environment Using LiDAR

Electronics ◽

10.3390/electronics8010043 ◽

2019 ◽

Vol 8 (1) ◽

pp. 43 ◽

Cited By ~ 6

Author(s):

Rendong Wang ◽

Youchun Xu ◽

Miguel Angel Sotelo ◽

Yulin Ma ◽

Thompson Sarkodie-Gyan ◽

...

Keyword(s):

Dynamic Environment ◽

Point Clouds ◽

Autonomous Driving ◽

Initial Position ◽

Urban Environments ◽

Data Set ◽

Registration Method ◽

Position Errors ◽

Initial Errors ◽

High Dynamic

The registration of point clouds in urban environments faces problems such as dynamic vehicles and pedestrians, changeable road environments, and GPS inaccuracies. The state-of-the-art methodologies have usually combined the dynamic object tracking and/or static feature extraction data into a point cloud towards the solution of these problems. However, there is the occurrence of minor initial position errors due to these methodologies. In this paper, the authors propose a fast and robust registration method that exhibits no need for the detection of any dynamic and/or static objects. This proposed methodology may be able to adapt to higher initial errors. The initial steps of this methodology involved the optimization of the object segmentation under the application of a series of constraints. Based on this algorithm, a novel multi-layer nested RANSAC algorithmic framework is proposed to iteratively update the registration results. The robustness and efficiency of this algorithm is demonstrated on several high dynamic scenes of both short and long time intervals with varying initial offsets. A LiDAR odometry experiment was performed on the KITTI data set and our extracted urban data-set with a high dynamic urban road, and the average of the horizontal position errors was compared to the distance traveled that resulted in 0.45% and 0.55% respectively.

Download Full-text

Registration method of point clouds using improved digital image correlation coefficient

Optical Engineering ◽

10.1117/1.oe.57.11.113104 ◽

2018 ◽

Vol 57 (11) ◽

pp. 1 ◽

Cited By ~ 3

Author(s):

Shi-Fan Liu

Keyword(s):

Digital Image Correlation ◽

Correlation Coefficient ◽

Digital Image ◽

Point Clouds ◽

Image Correlation ◽

Registration Method

Download Full-text

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.

Download Full-text