scholarly journals 2D Grid Map Compensation Using ICP Algorithm based on Feature Points

2015 ◽  
Vol 21 (10) ◽  
pp. 965-971
Author(s):  
Yu-Seop Hwang ◽  
Dong-Ju Lee ◽  
Ho-Yun Yu ◽  
Jang-Myung Lee
Keyword(s):  
Feature Points ◽  
Icp Algorithm ◽  
Grid Map

Surface Estimation ICP Algorithm for Building a 3D Map by a Scanning LRF

2017 ◽  
Vol 14 (03) ◽  
pp. 1750011
Author(s):  
Yoseop Hwang ◽  
Jangmyung Lee
Keyword(s):  
Mobile Robot ◽  
Robot Navigation ◽  
Three Dimensional ◽  
Mobile Robot Navigation ◽  
Laser Range Finder ◽  
Map Building ◽  
Feature Points ◽  
Icp Algorithm ◽  
Surface Conditions ◽  
Surface Estimation

A new three-dimensional (3D) map building method based on Laser Range Finder (LRF) has been proposed in this research, performing a surface estimation with the Iterative Closest Point (ICP) algorithm. While a mobile robot is navigating in an unknown environment, the entire environment cannot be scanned by LRF since kinematic features of the mobile robot and surface conditions are dynamically changing. To resolve this difficulty in building a 3D map while the mobile robot is navigating, a surface estimation ICP algorithm is proposed, which is based on the continuity of the environment around mobile robot. That is, this new algorithm recovers the un-scanned area by estimating feature points in the neighboring two regions based on the continuous environment information. The effectiveness of proposed algorithm has been demonstrated through real experiments of the mobile robot navigation.

scholarly journals 2D Grid Map Compensation using an ICP Algorithm

2014 ◽  
Vol 20 (11) ◽  
pp. 1170-1174 ◽  
Author(s):  
Dong-Ju Lee ◽  
Yu-Seop Hwang ◽  
Yeol-Min Yun ◽  
Jang-Myung Lee
Keyword(s):  
Icp Algorithm ◽  
Grid Map

scholarly journals Application of the Terrestrial Laser Scanning in Slope Deformation Monitoring: Taking a Highway Slope as an Example

2020 ◽  
Vol 10 (8) ◽  
pp. 2808 ◽  
Author(s):  
Chao Yin ◽  
Haoran Li ◽  
Zhinan Hu ◽  
Ying Li
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Terrestrial Laser Scanning ◽  
Deformation Monitoring ◽  
Slope Deformation ◽  
Model Parameters ◽  
Feature Points ◽  
Registration Method ◽  
Icp Algorithm ◽  
Fine Registration

Slope deformation monitoring is the prerequisite for disaster risk assessment and engineering control. Terrestrial laser scanning (TLS) is highly applicable to this field. Coarse registration method of point cloud based on scale-invariant feature transform (SIFT) feature points and fine registration method based on the k-dimensional tree (K-D tree) improved iterative closest point (ICP) algorithm were proposed. The results show that they were superior to other algorithms (such as speeded-up robust features (SURF) feature points, Harris feature points, and Levenberg-Marquardt (LM) improved ICP algorithm) when taking the Stanford Bunny as an example, and had high applicability in coarse and fine registration. In order to integrate the advantages of point measurement and surface measurement, an improved point cloud comparison method was proposed and the optimal model parameters were determined through model tests. A case study was conducted on the left side of the K146 + 150 point at S236 Boshan section, Shandong Province, and research results show that from 14 August 2018 and 9 November 2019, the overall deformation of the slope was small with a maximum value of 0.183 m, and the slope will continue to maintain a stable state without special inducing factors such as earthquake, heavy rainfall and artificial excavation.

Point Cloud Registration Algorithm Based on the ISS Feature Points Combined with Improved ICP Algorithm

2017 ◽  
Vol 54 (11) ◽  
pp. 111503 ◽  
Author(s):  
李仁忠 Li Renzhong ◽  
杨 曼 Yang Man ◽  
田 瑜 Tian Yu ◽  
刘阳阳 Liu Yangyang ◽  
张缓缓 Zhang Huanhuan
Keyword(s):  
Point Cloud ◽  
Feature Points ◽  
Point Cloud Registration ◽  
Icp Algorithm ◽  
Registration Algorithm

scholarly journals The Semi-dense ICP Algorithm Based on the SIFT Feature Points Neighborhood

2020 ◽  
Vol 1631 ◽  
pp. 012058
Author(s):  
Baichuan Han ◽  
Wei Wu ◽  
Yunfeng Wang ◽  
Jinfeng Liu
Keyword(s):  
Feature Points ◽  
Icp Algorithm ◽  
Sift Feature

Registration and Fusion of UAV LiDAR System Sequence Images and Laser Point Clouds

2020 ◽  
Author(s):  
Jiayong Yu ◽  
Longchen Ma ◽  
Maoyi Tian, ◽  
Xiushan Lu
Keyword(s):  
Point Cloud ◽  
Image Data ◽  
Point Clouds ◽  
Optical Image ◽  
Point Cloud Data ◽  
Feature Points ◽  
Lidar System ◽  
Registration Accuracy ◽  
Cloud Data ◽  
Intensity Image

The unmanned aerial vehicle (UAV)-mounted mobile LiDAR system (ULS) is widely used for geomatics owing to its efficient data acquisition and convenient operation. However, due to limited carrying capacity of a UAV, sensors integrated in the ULS should be small and lightweight, which results in decrease in the density of the collected scanning points. This affects registration between image data and point cloud data. To address this issue, the authors propose a method for registering and fusing ULS sequence images and laser point clouds, wherein they convert the problem of registering point cloud data and image data into a problem of matching feature points between the two images. First, a point cloud is selected to produce an intensity image. Subsequently, the corresponding feature points of the intensity image and the optical image are matched, and exterior orientation parameters are solved using a collinear equation based on image position and orientation. Finally, the sequence images are fused with the laser point cloud, based on the Global Navigation Satellite System (GNSS) time index of the optical image, to generate a true color point cloud. The experimental results show the higher registration accuracy and fusion speed of the proposed method, thereby demonstrating its accuracy and effectiveness.

FACE RECOGNITION USING FUZZY INFERENCE SYSTEM

2009 ◽  
Vol 8 (3) ◽  
pp. 887-897
Author(s):  
Vishal Paika ◽  
Er. Pankaj Bhambri
Keyword(s):  
Neural Networks ◽  
Fuzzy Inference ◽  
Facial Features ◽  
Facial Appearance ◽  
Feature Points ◽  
Inference System ◽  
Detection Techniques ◽  
The Face ◽  
Hair Style ◽  
Facial Images

The face is the feature which distinguishes a person. Facial appearance is vital for human recognition. It has certain features like forehead, skin, eyes, ears, nose, cheeks, mouth, lip, teeth etc which helps us, humans, to recognize a particular face from millions of faces even after a large span of time and despite large changes in their appearance due to ageing, expression, viewing conditions and distractions such as disfigurement of face, scars, beard or hair style. A face is not merely a set of facial features but is rather but is rather something meaningful in its form.In this paper, depending on the various facial features, a system is designed to recognize them. To reveal the outline of the face, eyes, ears, nose, teeth etc different edge detection techniques have been used. These features are extracted in the term of distance between important feature points. The feature set obtained is then normalized and are feed to artificial neural networks so as to train them for reorganization of facial images.

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