scholarly journals AutonomousWalking over Obstacles by Means of LRF for Hexapod Robot COMET-IV

2012 ◽  
Vol 24 (1) ◽  
pp. 55-63 ◽  
Author(s):  
Mohd Razali Daud ◽  
◽  
Kenzo Nonami ◽  
Keyword(s):  
Autonomous Navigation ◽  
Point Cloud ◽  
Cell Model ◽  
Search Algorithm ◽  
Grid Cell ◽  
Point Cloud Data ◽  
Hexapod Robot ◽  
Cloud Data ◽  
Grid Based

This paper presents an autonomous navigation system for a hydraulically driven hexapod robot (COMETIV) based on point cloud data acquired using a rotating Laser Range Finder (LRF). The size of the robot would prohibit its movement in a stochastic terrain environment if we only consider letting it avoid obstacles. However, the robot has a unique ability to walk over obstacles. We thus proposed the so-called Grid-based Walking Trajectory for Legged Robot (GWTLR) method. The method is developed on the basis of the geometric representation of a stochastic terrain in terms of grid cell characteristics. We also introduced the “Grid-cell model for COMET-IV” to assess the characteristics of the grid cells and to determine whether each of the cells is traversable or not. Finally, the shortest safe walking trajectory is generated using a search algorithm, A*. The performance of the proposed method is verified by the experimental results of the successful determination of a walking trajectory path and by completely walking over obstacles in various arrangements.

scholarly journals Feature Extraction from 3D Point Cloud Data Based on Discrete Curves

2013 ◽  
Vol 2013 ◽  
pp. 1-19 ◽  
Author(s):  
Yi An ◽  
Zhuohan Li ◽  
Cheng Shao
Keyword(s):  
Feature Extraction ◽  
Autonomous Navigation ◽  
Point Cloud ◽  
Geometric Features ◽  
3D Point Cloud ◽  
Point Cloud Data ◽  
Principal Curvatures ◽  
Cloud Data ◽  
Discrete Curves ◽  
Threshold Setting

Reliable feature extraction from 3D point cloud data is an important problem in many application domains, such as reverse engineering, object recognition, industrial inspection, and autonomous navigation. In this paper, a novel method is proposed for extracting the geometric features from 3D point cloud data based on discrete curves. We extract the discrete curves from 3D point cloud data and research the behaviors of chord lengths, angle variations, and principal curvatures at the geometric features in the discrete curves. Then, the corresponding similarity indicators are defined. Based on the similarity indicators, the geometric features can be extracted from the discrete curves, which are also the geometric features of 3D point cloud data. The threshold values of the similarity indicators are taken from[0,1], which characterize the relative relationship and make the threshold setting easier and more reasonable. The experimental results demonstrate that the proposed method is efficient and reliable.

scholarly journals Intelligent crack extraction based on terrestrial laser scanning measurement

2020 ◽  
Vol 53 (3-4) ◽  
pp. 416-426 ◽  
Author(s):  
Hao Yang ◽  
Xiangyang Xu
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Terrestrial Laser Scanning ◽  
Point Cloud Data ◽  
Time Cost ◽  
Visual Examination ◽  
Average Width ◽  
Cloud Data ◽  
Novel Method

The hazards of cracks, which could badly decrease reliability and safety of structures, are receiving increasing attention with the popularity of tunnel constructions. Traditional crack inspection relies on visual examination, which is time-, cost- and labor-intensive. Therefore, how to identify and measure cracks intelligently is significantly essential. The paper focuses on the Canny method to extract cracks of tunnel structures by the intensity value of reflectivity. We propose and investigate a novel method which combines dilation and the Canny algorithm to identify and extract the cracks automatically and intelligently based on the point cloud data of terrestrial laser scanning measurement. In order for measurement of cracks, the projection of summed edge pixels is adopted, where a synthesis is carried out on the detection results with all sampling parameters. Based on the synthesized image, vertical crack presents two sharp peaks where the space of the peaks indicates the average width of the crack, as well as its position. The advantage of the method is that it does not require determination of Canny detector parameters. The deviation between manual measurement and Canny detection is 2.92%.

Study on a Nearest Neighbor Algorithm for Point Clouds Based on the Space Partition Principle

2012 ◽  
Vol 201-202 ◽  
pp. 834-837
Author(s):  
Xue Chang Zhang ◽  
Tao Liang ◽  
Yan Mei Tang ◽  
Xu Zhang
Keyword(s):  
Point Cloud ◽  
Nearest Neighbor ◽  
Search Algorithm ◽  
Point Clouds ◽  
Point Cloud Data ◽  
Product Modeling ◽  
Cloud Data ◽  
Space Partition ◽  
Range Tree ◽  
Partition Principle

In the product modeling based on the reverse engineering, the point cloud data smoothing and multi-view point cloud data registration will be related to search some nearest neighbor points. The search speed will determine the efficiency of product modeling in some cases. The paper analysis the nearest neighbor point query algorithms, KD tree and Range tree, based on the space partition principle. The tree structure creation and query method are described by pseudo-code in the paper. Finally, the experimental results involving different sizes point clouds demonstrate that KD tree and Range tree have their own advantages in space storage and time complexity. Two data strictures all meet the efficiency of the search algorithm.

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.

ESTIMATION OF VEGETATION DENSITY OF TREES USING ALB POINT CLOUD DATA

2018 ◽  
Vol 74 (4) ◽  
pp. I_547-I_552
Author(s):  
Keisuke YOSHIDA ◽  
Shiro MAENO ◽  
Syuhei OGAWA ◽  
Sadayuki ISEKI ◽  
Ryosuke AKOH
Keyword(s):  
Point Cloud ◽  
Point Cloud Data ◽  
Vegetation Density ◽  
Cloud Data
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