scholarly journals Fast Ground Segmentation for 3D LiDAR Point Cloud Based on Jump-Convolution-Process

2021 ◽  
Vol 13 (16) ◽  
pp. 3239
Author(s):  
Zhihao Shen ◽  
Huawei Liang ◽  
Linglong Lin ◽  
Zhiling Wang ◽  
Weixin Huang ◽  
...  
Keyword(s):  
Autonomous Vehicles ◽  
Point Cloud ◽  
Driving Safety ◽  
Training Requirements ◽  
Detection Technology ◽  
Extraction Algorithm ◽  
Convolution Process ◽  
Ground Segmentation ◽  
Scan Data ◽  
Local Feature Extraction

LiDAR occupies a vital position in self-driving as the advanced detection technology enables autonomous vehicles (AVs) to obtain much environmental information. Ground segmentation for LiDAR point cloud is a crucial procedure to ensure AVs’ driving safety. However, some current algorithms suffer from embarrassments such as unavailability on complex terrains, excessive time and memory usage, and additional pre-training requirements. The Jump-Convolution-Process (JCP) is proposed to solve these issues. JCP converts the segmentation problem of the 3D point cloud into the smoothing problem of the 2D image and takes little time to improve the segmentation effect significantly. First, the point cloud marked by an improved local feature extraction algorithm is projected onto an RGB image. Then, the pixel value is initialized with the points’ label and continuously updated according to image convolution. Finally, a jump operation is introduced in the convolution process to perform calculations only on the low-confidence points filtered by the credibility propagation algorithm, reducing the time cost. Experiments on three datasets show that our approach has a better segmentation accuracy and terrain adaptability than those of the three existing methods. Meanwhile, the average time for the proposed method to deal with one scan data of 64-beam and 128-beam LiDAR is only 8.61 ms and 15.62 ms, which fully meets the AVs’ requirement for real-time performance.

Robust-PCA-based hierarchical plane extraction for application to geometric 3D indoor mapping

2014 ◽  
Vol 41 (2) ◽  
pp. 203-212 ◽  
Author(s):  
Suyong Yeon ◽  
ChangHyun Jun ◽  
Hyunga Choi ◽  
Jaehyeon Kang ◽  
Youngmok Yun ◽  
...  
Keyword(s):  
Principal Component ◽  
Point Clouds ◽  
Previous Method ◽  
Divide And Conquer ◽  
Sensor Data ◽  
Estimation Accuracy ◽  
Content Type ◽  
Extraction Algorithm ◽  
Plane Extraction ◽  
Scan Data

Purpose – The authors aim to propose a novel plane extraction algorithm for geometric 3D indoor mapping with range scan data. Design/methodology/approach – The proposed method utilizes a divide-and-conquer step to efficiently handle huge amounts of point clouds not in a whole group, but in forms of separate sub-groups with similar plane parameters. This method adopts robust principal component analysis to enhance estimation accuracy. Findings – Experimental results verify that the method not only shows enhanced performance in the plane extraction, but also broadens the domain of interest of the plane registration to an information-poor environment (such as simple indoor corridors), while the previous method only adequately works in an information-rich environment (such as a space with many features). Originality/value – The proposed algorithm has three advantages over the current state-of-the-art method in that it is fast, utilizes more inlier sensor data that does not become contaminated by severe sensor noise and extracts more accurate plane parameters.

scholarly journals KVGCN: A KNN Searching and VLAD Combined Graph Convolutional Network for Point Cloud Segmentation

2021 ◽  
Vol 13 (5) ◽  
pp. 1003
Author(s):  
Nan Luo ◽  
Hongquan Yu ◽  
Zhenfeng Huo ◽  
Jinhui Liu ◽  
Quan Wang ◽  
...  
Keyword(s):  
Point Cloud ◽  
Nearest Neighbor ◽  
Semantic Segmentation ◽  
K Nearest Neighbor ◽  
Topological Graph ◽  
Convolutional Network ◽  
Cloud Data ◽  
Nearest Neighbor Searching ◽  
Point Cloud Segmentation ◽  
Local Feature Extraction

Semantic segmentation of the sensed point cloud data plays a significant role in scene understanding and reconstruction, robot navigation, etc. This work presents a Graph Convolutional Network integrating K-Nearest Neighbor searching (KNN) and Vector of Locally Aggregated Descriptors (VLAD). KNN searching is utilized to construct the topological graph of each point and its neighbors. Then, we perform convolution on the edges of constructed graph to extract representative local features by multiple Multilayer Perceptions (MLPs). Afterwards, a trainable VLAD layer, NetVLAD, is embedded in the feature encoder to aggregate the local and global contextual features. The designed feature encoder is repeated for multiple times, and the extracted features are concatenated in a jump-connection style to strengthen the distinctiveness of features and thereby improve the segmentation. Experimental results on two datasets show that the proposed work settles the shortcoming of insufficient local feature extraction and promotes the accuracy (mIoU 60.9% and oAcc 87.4% for S3DIS) of semantic segmentation comparing to existing models.

A Closed Point Cloud Edge Extraction Algorithm Using Edge Coefficient

2018 ◽  
Vol 55 (11) ◽  
pp. 111003
Author(s):  
韩玉川 Han Yuchuan ◽  
侯贺 Hou He ◽  
白云瑞 Bai Yunrui ◽  
朱险峰 Zhu Xianfeng
Keyword(s):  
Point Cloud ◽  
Edge Extraction ◽  
Closed Point ◽  
Extraction Algorithm

scholarly journals Real-Time Vehicle Detection Algorithm Based on Vision and Lidar Point Cloud Fusion

2019 ◽  
Vol 2019 ◽  
pp. 1-9 ◽  
Author(s):  
Hai Wang ◽  
Xinyu Lou ◽  
Yingfeng Cai ◽  
Yicheng Li ◽  
Long Chen
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Autonomous Vehicles ◽  
Point Cloud ◽  
Vehicle Detection ◽  
Detection Algorithm ◽  
Detection Methods ◽  
Depth Information ◽  
Detection Accuracy ◽  
Classification Rate

Vehicle detection is one of the most important environment perception tasks for autonomous vehicles. The traditional vision-based vehicle detection methods are not accurate enough especially for small and occluded targets, while the light detection and ranging- (lidar-) based methods are good in detecting obstacles but they are time-consuming and have a low classification rate for different target types. Focusing on these shortcomings to make the full use of the advantages of the depth information of lidar and the obstacle classification ability of vision, this work proposes a real-time vehicle detection algorithm which fuses vision and lidar point cloud information. Firstly, the obstacles are detected by the grid projection method using the lidar point cloud information. Then, the obstacles are mapped to the image to get several separated regions of interest (ROIs). After that, the ROIs are expanded based on the dynamic threshold and merged to generate the final ROI. Finally, a deep learning method named You Only Look Once (YOLO) is applied on the ROI to detect vehicles. The experimental results on the KITTI dataset demonstrate that the proposed algorithm has high detection accuracy and good real-time performance. Compared with the detection method based only on the YOLO deep learning, the mean average precision (mAP) is increased by 17%.

scholarly journals Visual Detection of Surface Defects Based on Self-Feature Comparison in Robot 3-D Printing

2019 ◽  
Vol 10 (1) ◽  
pp. 235 ◽  
Author(s):  
Hongyao Shen ◽  
Wangzhe Du ◽  
Weijun Sun ◽  
Yuetong Xu ◽  
Jianzhong Fu
Keyword(s):  
Feature Extraction ◽  
3D Printing ◽  
Defect Detection ◽  
Point Cloud ◽  
Fused Deposition Modeling ◽  
Surface Defects ◽  
Detection Accuracy ◽  
Feature Extraction Method ◽  
Fused Deposition ◽  
Detection Technology

Fused Deposition Modeling (FDM) additive manufacturing technology is widely applied in recent years. However, there are many defects that may affect the surface quality, accuracy, or even cause the collapse of the parts in the printing process. In the existing defect detection technology, the characteristics of parts themselves may be misjudged as defects. This paper presents a solution to the problem of distinguishing the defects and their own characteristics in robot 3-D printing. A self-feature extraction method of shape defect detection of 3D printing products is introduced. Discrete point cloud after model slicing is used both for path planning in 3D printing and self-feature extraction at the same time. In 3-D printing, it can generate G-code and control the shooting direction of the camera. Once the current coordinates have been received, the self-feature extraction begins, whose key steps are keeping a visual point cloud of the printed part and projecting the feature points to the picture under the equal mapping condition. After image processing technology, the contours of pictured projected and picture captured will be detected. At last, the final defects can be identified after evaluation of contour similarity based on empirical formula. This work will help to detect the defects online, improve the detection accuracy, and reduce the false detection rate without being affected by its own characteristics.

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