scholarly journals A Practical Point Cloud Based Road Curb Detection Method for Autonomous Vehicle

Information ◽  
2017 ◽  
Vol 8 (3) ◽  
pp. 93 ◽  
Author(s):  
Rulin Huang ◽  
Jiajia Chen ◽  
Jian Liu ◽  
Lu Liu ◽  
Biao Yu ◽  
...  
Keyword(s):  
Point Cloud ◽  
Detection Method ◽  
Autonomous Vehicle ◽  
Practical Point

scholarly journals Evaluation of 3D point cloud for autonomous vehicle（3D point cloud evaluation index for detecting the double structure）

2020 ◽  
Vol 86 (892) ◽  
pp. 20-00151-20-00151
Author(s):  
Takaya MURAKAMI ◽  
Yuki KITSUKAWA ◽  
Eijiro TAKEUCHI ◽  
Yoshiki NINOMIYA ◽  
Junichi MEGURO
Keyword(s):  
Point Cloud ◽  
Autonomous Vehicle ◽  
Evaluation Index ◽  
3D Point Cloud ◽  
Double Structure

scholarly journals Estimating Autonomous Vehicle Localization Error Using 2D Geographic Information

2019 ◽  
Vol 8 (6) ◽  
pp. 288 ◽  
Author(s):  
Kelvin Wong ◽  
Ehsan Javanmardi ◽  
Mahdi Javanmardi ◽  
Shunsuke Kamijo
Keyword(s):  
Point Cloud ◽  
Autonomous Vehicle ◽  
Autonomous Driving ◽  
Geographic Information ◽  
Localization Error ◽  
Random Forest Regression ◽  
Vehicle Localization ◽  
Surrounding Environment ◽  
Tightly Coupled ◽  
Critical Requirement

Accurately and precisely knowing the location of the vehicle is a critical requirement for safe and successful autonomous driving. Recent studies suggest that error for map-based localization methods are tightly coupled with the surrounding environment. Considering this relationship, it is therefore possible to estimate localization error by quantifying the representation and layout of real-world phenomena. To date, existing work on estimating localization error have been limited to using self-collected 3D point cloud maps. This paper investigates the use of pre-existing 2D geographic information datasets as a proxy to estimate autonomous vehicle localization error. Seven map evaluation factors were defined for 2D geographic information in a vector format, and random forest regression was used to estimate localization error for five experiment paths in Shinjuku, Tokyo. In the best model, the results show that it is possible to estimate autonomous vehicle localization error with 69.8% of predictions within 2.5 cm and 87.4% within 5 cm.

scholarly journals Incremental 3D Cuboid Modeling with Drift Compensation

Sensors ◽  
2019 ◽  
Vol 19 (1) ◽  
pp. 178 ◽  
Author(s):  
Masashi Mishima ◽  
Hideaki Uchiyama ◽  
Diego Thomas ◽  
Rin-ichiro Taniguchi ◽  
Rafael Roberto ◽  
...  
Keyword(s):  
Augmented Reality ◽  
Point Cloud ◽  
Detection Method ◽  
False Detection ◽  
Cluttered Environments ◽  
Plane Map ◽  
Localization And Mapping ◽  
Cad Models ◽  
Drift Compensation ◽  
Sequential Measurements

This paper presents a framework of incremental 3D cuboid modeling by using the mapping results of an RGB-D camera based simultaneous localization and mapping (SLAM) system. This framework is useful in accurately creating cuboid CAD models from a point cloud in an online manner. While performing the RGB-D SLAM, planes are incrementally reconstructed from a point cloud in each frame to create a plane map. Then, cuboids are detected in the plane map by analyzing the positional relationships between the planes, such as orthogonality, convexity, and proximity. Finally, the position, pose, and size of a cuboid are determined by computing the intersection of three perpendicular planes. To suppress the false detection of the cuboids, the cuboid shapes are incrementally updated with sequential measurements to check the uncertainty of the cuboids. In addition, the drift error of the SLAM is compensated by the registration of the cuboids. As an application of our framework, an augmented reality-based interactive cuboid modeling system was developed. In the evaluation at cluttered environments, the precision and recall of the cuboid detection were investigated, compared with a batch-based cuboid detection method, so that the advantages of our proposed method were clarified.

scholarly journals An Innovative Detection Method of High-Speed Railway Track Slab Supporting Block Plane Based on Point Cloud Data from 3D Scanning Technology

2019 ◽  
Vol 9 (16) ◽  
pp. 3345 ◽  
Author(s):  
Chen ◽  
Qin ◽  
Xia ◽  
Bao ◽  
Huang ◽  
...  
Keyword(s):  
Point Cloud ◽  
High Speed ◽  
Detection Method ◽  
Railway Track ◽  
Point Cloud Data ◽  
3D Scanner ◽  
High Speed Railway ◽  
Cloud Data ◽  
Plane Point ◽  
Track Slab

The dimension detection of high-speed railway track slabs is one of the most important tasks before the track slabs delivery. Based on the characteristics of a 3D scanner which can acquire a large amount of measurement data continuously and rapidly in a short time, this paper uses the integration of 3D scanner and the intelligent robot to detect the CRTSIII (China Railway Track System) track slab supporting block plane, then the dense and accurate supporting block plane point cloud data is obtained, and the point cloud data is registered with the established model. An improved Random Sample Consensus (RANSAC) plane fitting algorithm is also proposed to extract the data of supporting block plane point cloud in this paper. The detection method is verified and the quality analysis of the detection results is assessed by a lot of real point cloud data obtained on site. The results show that the method can meet the quality control of CRTSIII finished track slab and the detection standard. Compared with the traditional detection methods, the detection method proposed in this paper can complete the detection of a track slab in 7 min, which greatly improves the detection efficiency, and has better reliability. The method has wide application prospects in the field of railway component detection.

scholarly journals Research on the Detection Method of Implicit Self Symmetry in a High-Level Semantic Model

Symmetry ◽  
2019 ◽  
Vol 12 (1) ◽  
pp. 28 ◽  
Author(s):  
Chao Wang
Keyword(s):  
Point Cloud ◽  
Detection Method ◽  
Cloud Model ◽  
High Reliability ◽  
Semantic Model ◽  
Symmetry Detection ◽  
Model Surface ◽  
Point Cloud Model ◽  
High Level ◽  
Skeleton Point

In order to improve the accuracy of semantic model intrinsic detection, a skeleton-based high-level semantic model intrinsic self-symmetry detection method is proposed. The semantic analysis of the model set is realized by the uniform segmentation of the model within the same style, the component correspondence of the model between different styles, and the shape content clustering. Based on the results of clustering analysis, for a given three-dimensional (3D) point cloud model, according to the curve skeleton, the skeleton point pairs reflecting the symmetry between the model surface points are obtained by the election method, and the symmetry is extended to the model surface vertices according to these skeleton point pairs. With the help of skeleton, the symmetry of the point cloud model is obtained, and then the symmetry region of point cloud model is obtained by the symmetric correspondence matrix and spectrum method, so as to realize the intrinsic symmetry detection of the model. The experimental results show that the proposed method has the advantages of less time, high accuracy, and high reliability.

scholarly journals Intelligent Point Cloud Edge Detection Method Based on Projection Transformation

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Juan Zhu ◽  
Xiaofeng Yue ◽  
Jipeng Huang ◽  
Zongwei Huang
Keyword(s):  
Edge Detection ◽  
Point Cloud ◽  
Detection Method ◽  
Target Point ◽  
Normal Vector ◽  
Edge Point ◽  
Vertical Projection ◽  
Cloud Data ◽  
Edge Detection Method ◽  
Projection Transformation

An edge detection method based on projection transformation is proposed. First, the vertical projection transformation is carried out on the target point cloud. Data X and data Y are normalized to the width and height of the image, respectively. Data Z is normalized to the range of 0-255, and the depth represents the gray level of the image. Then, the Canny algorithm is used to detect the edge of the projection transformed image, and the detected edge data is back projected to extract the edge point cloud in the point cloud. Evaluate the performance by calculating the normal vector of the edge point cloud. Compared with the normal vector of the whole data point cloud of the target, the normal vector of the edge point cloud can well express the characteristics of the target, and the calculation time is reduced to 10% of the original.

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