scholarly journals Vehicle pose estimation algorithm for parking automated guided vehicle

2020 ◽  
Vol 17 (1) ◽  
pp. 172988141989133
Author(s):  
Zhixiong Ning ◽  
Xin Wang ◽  
Jun Wang ◽  
Huafeng Wen
Keyword(s):  
Pose Estimation ◽  
Symmetry Plane ◽  
Point Clouds ◽  
Vehicle Body ◽  
Iterative Closest Point ◽  
Support Vector ◽  
Automated Guided Vehicle ◽  
Alignment Algorithm ◽  
Registration Method ◽  
Iterative Closest Point Algorithm

Parking automated guided vehicle is more and more widely used for efficient automatic parking and one of the tough challenges for parking automated guided vehicle is the problem of vehicle pose estimation. The traditional algorithms rely on the profile information of vehicle body and sensors are required to be mounted at the top of the vehicle. However, the sensors are always mounted at a lower place because the height of a parking automated guided vehicle is always beyond 0.2mm, where we can only get the vehicle wheel information and limited vehicle body information. In this article, a novel method is given based on the symmetry of wheel point clouds collected by 3-D lidar. Firstly, we combine cell-based method with support vector machine classifier to segment ground point clouds. Secondly, wheel point clouds are segmented from obstacle point clouds and their symmetry are corrected by iterative closest point algorithm. Then, we estimate the vehicle pose by the symmetry plane of wheel point clouds. Finally, we compare our method with registration method that combines sample consensus initial alignment algorithm and iterative closest point algorithm. The experiments have been carried out.

Points Registration for Roadside LiDAR Sensors

2019 ◽  
Vol 2673 (9) ◽  
pp. 627-639 ◽  
Author(s):  
Jianqing Wu ◽  
Hao Xu ◽  
Wei Liu
Keyword(s):  
Ground Surface ◽  
Cost Effective ◽  
Point Clouds ◽  
Iterative Closest Point ◽  
Connected Vehicle ◽  
Point Selection ◽  
Registration Method ◽  
Data Registration ◽  
The Difference ◽  
Iterative Closest Point Algorithm

Roadside LiDAR deployment provides a solution to obtain the real-time high-resolution micro traffic data of unconnected road users for the connected-vehicle road network. Single roadside LiDAR sensor has a lot of limitations considering the scant coverage and the difficulty of handling object occlusion issue. Multiple roadside LiDAR sensors can provide a larger coverage and eliminate the object occlusion issue. To combine different LiDAR sensors, it is necessary to integrate the point clouds into the same coordinate system. The existing points registration methods serving mapping scans or autonomous sensing systems could not be directly used for roadside LiDAR sensors considering the different feature of point clouds and the spare points in the cost-effective roadside LiDAR sensors. This paper developed an approach for roadside LiDAR points registration. The developed points-aggregation-based partial iterative closest point algorithm (PA-PICP) is a semi-automatic points registration method, which contains two major parts: XY data registration and Z adjustment. A semi-automatic key point selection method was introduced. The partial iterative closest point was applied to minimize the difference between different LiDARs in the XY plane. The intersection of ground surface between different LiDARs was used for Z-axis adjustment. The performance of the developed procedure was evaluated with field-collected LiDAR data. The results showed the effectiveness and accuracy of data integration using PA-PICP was greatly improved compared with points registration using the traditional iterative closest point. The case studies also showed that the occlusion issue can be fixed after PA-PICP points registration.

scholarly journals ITERATIVE CLOSEST POINT ALGORITHM FOR ACCURATE REGISTRATION OF COARSELY REGISTERED POINT CLOUDS WITH CITYGML MODELS

2019 ◽  
Vol IV-2/W5 ◽  
pp. 201-208 ◽  
Author(s):  
S. Goebbels ◽  
R. Pohle-Fröhlich ◽  
P. Pricken
Keyword(s):  
Point Cloud ◽  
Point Clouds ◽  
Iteration Step ◽  
Iterative Closest Point ◽  
Target Point ◽  
Standard Tool ◽  
Iterative Closest Point Algorithm ◽  
Test Scenarios ◽  
Plane Mode ◽  
City Models

<p><strong>Abstract.</strong> The Iterative Closest Point algorithm (ICP) is a standard tool for registration of a source to a target point cloud. In this paper, ICP in point-to-plane mode is adopted to city models that are defined in CityGML. With this new point-to-model version of the algorithm, a coarsely registered photogrammetric point cloud can be matched with buildings’ polygons to provide, e.g., a basis for automated 3D facade modeling. In each iteration step, source points are projected to these polygons to find correspondences. Then an optimization problem is solved to find an affine transformation that maps source points to their correspondences as close as possible. Whereas standard ICP variants do not perform scaling, our algorithm is capable of isotropic scaling. This is necessary because photogrammetric point clouds obtained by the structure from motion algorithm typically are scaled randomly. Two test scenarios indicate that the presented algorithm is faster than ICP in point-to-plane mode on sampled city models.</p>

An Iterative Closest Point Algorithm Based on Biunique Correspondence of Point Clouds for 3D Reconstruction

2015 ◽  
Vol 35 (5) ◽  
pp. 0515003 ◽  
Author(s):  
韦盛斌 Wei Shengbin ◽  
王少卿 Wang Shaoqing ◽  
周常河 Zhou Changhe ◽  
刘昆 Liu Kun ◽  
范鑫 Fan Xin
Keyword(s):  
3D Reconstruction ◽  
Point Clouds ◽  
Iterative Closest Point ◽  
Iterative Closest Point Algorithm

Application of the 'Iterative Closest Point' Algorithm for Determination of Rotation Axis of a Turntable

2013 ◽  
Vol 199 ◽  
pp. 273-278
Author(s):  
Ireneusz Wróbel
Keyword(s):  
Reverse Engineering ◽  
Point Cloud ◽  
Rotation Axis ◽  
Rapid Development ◽  
Point Clouds ◽  
Iterative Closest Point ◽  
Mechanical Parts ◽  
Scanning Process ◽  
Iterative Closest Point Algorithm

Reverse engineering [ is a field of technology which has been under rapid development for several recent years. Optic scanners are basic devices used as reverse engineering tools. Point cloud describes the shape of a scanned object. Automatic turntable is a device which enables a scanning process from different viewing angles. In the paper, the algorithm is described which has been used for determination of rotation axis of a turntable. The obtained axis constitutes the base for an aggregation of particular point clouds into single resultant common cloud describing the shape of the scanned object. Usability of this algorithm for precise scanning of mechanical parts was validated, precision of shape replication was also evaluated.

Accurate Registration of 3D Point Clouds Based on Keypoint Extraction and Improved Iterative Closest Point Algorithm

2020 ◽  
Vol 57 (6) ◽  
pp. 061002
Author(s):  
彭真 Peng Zhen ◽  
吕远健 Lü Yuanjian ◽  
渠超 Qu Chao ◽  
朱大虎 Zhu Dahu
Keyword(s):  
Point Clouds ◽  
Iterative Closest Point ◽  
3D Point Clouds ◽  
Iterative Closest Point Algorithm

Accurate registration of point clouds of damaged aero-engine blades

2020 ◽  
pp. 1-28
Author(s):  
Hamid Ghorbani ◽  
Farbod Khameneifar
Keyword(s):  
Hausdorff Distance ◽  
Point Cloud ◽  
Point Clouds ◽  
Superior Performance ◽  
Alignment Algorithm ◽  
Cad Model ◽  
Registration Method ◽  
Nominal Model ◽  
Data Points ◽  
Scan Data

Abstract This paper presents a novel method for aligning the scanned point clouds of damaged blades with their nominal CAD model. To inspect a damaged blade, the blade surface is scanned and the scan data in the form of a point cloud is compared to the nominal CAD model of the blade. To be able to compare the two surfaces, the scanned point cloud and the CAD model must be brought to the same coordinate system via a registration algorithm. The geometric nonconformity between the scanned point cloud and the nominal model stemmed from the damaged regions can affect the registration (alignment) outcome. The alignment errors then cause wrong inspection results. To prevent this from happening, the data points from the damaged regions have to be removed from the alignment calculations. The proposed registration method in this work can accurately and automatically eliminate the unreliable scanned data points of the damaged regions from the registration process. The main feature is a correspondence search technique based on the geometric properties of the local neighborhood of points. By combining the average curvature Hausdorff distance and average Euclidean Hausdorff distance, a metric is defined to locally measure the dissimilarities between the scan data and the nominal model and progressively remove the identified unreliable data points of the damaged regions with each iteration of the fine-tuned alignment algorithm. Implementation results have demonstrated that the proposed method is accurate and robust to noise with superior performance in comparison with the existing methods.

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

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.

Iterative closest point for accurate plane detection in unorganized point clouds

2021 ◽  
Vol 125 ◽  
pp. 103610 ◽  
Author(s):  
Cedrique Fotsing ◽  
Nareph Menadjou ◽  
Christophe Bobda
Keyword(s):  
Point Clouds ◽  
Iterative Closest Point
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