Analysis of Data Point Cloud Preprocessing and Feature Angle Detection Algorithm

2021 ◽  
Vol 14 ◽  
Author(s):  
Feng Zhao ◽  
Gaurav Dhiman
Keyword(s):  
Reverse Engineering ◽  
Mesh Generation ◽  
Point Cloud ◽  
Detection Algorithm ◽  
Corner Detection ◽  
Second Stage ◽  
Extraction Algorithm ◽  
Support Area ◽  
Segment Data ◽  
Penalty Weight

Background: The two main stages are utilized for feature extraction, from which the first stage consists of a penalty weight to the neighbor graph’s edges. The edge penalty weights are minimized by the neighbor sub-graph extraction to produce the set of feature patterns. For noisy data, the second stage is helpful. Methodology: In order to realize the measurement of the geometric dimensions of the ship block, this paper uses the theory of computer vision and reverse engineering to obtain the data of the segmented-hull with the method of digitizing the physical parts based on the vision, and processes the data by using the relevant knowledge of reverse engineering. Result: The results show that the efficiency of the edge extraction algorithm based on mathematical morphology is 30% higher than that of the mesh generation method. An adaptive corner detection algorithm based on the edge can adaptively determine the size of the support area and accurately detect the corner position. Conclusion: According to the characteristics of the point cloud of ship hull segment data, an adaptive corner detection algorithm based on the edge is adopted to verify its feasibility.

scholarly journals Emergency Landing Spot Detection Algorithm for Unmanned Aerial Vehicles

2021 ◽  
Vol 13 (10) ◽  
pp. 1930
Author(s):  
Gabriel Loureiro ◽  
André Dias ◽  
Alfredo Martins ◽  
José Almeida
Keyword(s):  
Point Cloud ◽  
Principal Component ◽  
Detection Algorithm ◽  
Cloud Data ◽  
Safety Issues ◽  
Spot Detection ◽  
Cloud Clusters ◽  
Aerial Vehicle ◽  
Incoming Light ◽  
Landing Spot

The use and research of Unmanned Aerial Vehicle (UAV) have been increasing over the years due to the applicability in several operations such as search and rescue, delivery, surveillance, and others. Considering the increased presence of these vehicles in the airspace, it becomes necessary to reflect on the safety issues or failures that the UAVs may have and the appropriate action. Moreover, in many missions, the vehicle will not return to its original location. If it fails to arrive at the landing spot, it needs to have the onboard capability to estimate the best area to safely land. This paper addresses the scenario of detecting a safe landing spot during operation. The algorithm classifies the incoming Light Detection and Ranging (LiDAR) data and store the location of suitable areas. The developed method analyses geometric features on point cloud data and detects potential right spots. The algorithm uses the Principal Component Analysis (PCA) to find planes in point cloud clusters. The areas that have a slope less than a threshold are considered potential landing spots. These spots are evaluated regarding ground and vehicle conditions such as the distance to the UAV, the presence of obstacles, the area’s roughness, and the spot’s slope. Finally, the output of the algorithm is the optimum spot to land and can vary during operation. The proposed approach evaluates the algorithm in simulated scenarios and an experimental dataset presenting suitability to be applied in real-time operations.

An Improved Corner Detection Algorithm Based on Harris

2018 ◽  
Author(s):  
Songqi Han ◽  
Weibo Yu ◽  
Hongtao Yang ◽  
Shicheng Wan
Keyword(s):  
Detection Algorithm ◽  
Corner Detection

Improved corner detection algorithm based on SUSAN principle

2007 ◽  
Author(s):  
Desen Yin ◽  
Yuejin Zhao ◽  
Bin Wang ◽  
Qian Song ◽  
Rongrong Cheng
Keyword(s):  
Detection Algorithm ◽  
Corner Detection

User-Driven Computer-Assisted Reverse Engineering of Editable CAD Assembly Models

2021 ◽  
pp. 1-16
Author(s):  
Ghazanfar Ali Shah ◽  
Jean-Philippe Pernot ◽  
Arnaud Polette ◽  
Franca Giannini ◽  
Marina Monti
Keyword(s):  
Reverse Engineering ◽  
Point Cloud ◽  
Point Clouds ◽  
Geometric Constraints ◽  
Computer Assisted ◽  
Reconstruction Process ◽  
Mechanical Parts ◽  
Starting Point ◽  
Cad Models ◽  
Incomplete Datasets

Abstract This paper introduces a novel reverse engineering technique for the reconstruction of editable CAD models of mechanical parts' assemblies. The input is a point cloud of a mechanical parts' assembly that has been acquired as a whole, i.e. without disassembling it prior to its digitization. The proposed framework allows for the reconstruction of the parametric CAD assembly model through a multi-step reconstruction and fitting approach. It is modular and it supports various exploitation scenarios depending on the available data and starting point. It also handles incomplete datasets. The reconstruction process starts from roughly sketched and parameterized geometries (i.e 2D sketches, 3D parts or assemblies) that are then used as input of a simulated annealing-based fitting algorithm, which minimizes the deviation between the point cloud and the reconstructed geometries. The coherence of the CAD models is maintained by a CAD modeler that performs the updates and satisfies the geometric constraints as the fitting process goes on. The optimization process leverages a two-level filtering technique able to capture and manage the boundaries of the geometries inside the overall point cloud in order to allow for local fitting and interfaces detection. It is a user-driven approach where the user decides what are the most suitable steps and sequence to operate. It has been tested and validated on both real scanned point clouds and as-scanned virtually generated point clouds incorporating several artifacts that would appear with real acquisition devices.

Pedestrian detection algorithm based on video sequences and laser point cloud

2014 ◽  
Vol 9 (3) ◽  
pp. 402-414 ◽  
Author(s):  
Hui Li ◽  
Yun Liu ◽  
Shengwu Xiong ◽  
Lin Wang
Keyword(s):  
Point Cloud ◽  
Pedestrian Detection ◽  
Detection Algorithm ◽  
Video Sequences

scholarly journals A Corner Detection Algorithm Based on Regional Center of Mass in Imaging through Water Surface

2021 ◽  
Vol 2083 (3) ◽  
pp. 032090
Author(s):  
Changli Mai ◽  
Bijian Jian ◽  
Yongfa Ling
Keyword(s):  
Water Surface ◽  
Center Of Mass ◽  
Detection Algorithm ◽  
Corner Detection ◽  
Random Fluctuation ◽  
False Detection ◽  
Detection Algorithms ◽  
Regional Center ◽  
Underwater Environment ◽  
Active Imaging

Abstract Structural light active imaging can obtain more information about the target scene, which is widely used in image registration,3D reconstruction of objects and motion detection. Due to the random fluctuation of water surface and complex underwater environment, the current corner detection algorithm has the problems of false detection and uncertainty. This paper proposes a corner detection algorithm based on the region centroid extraction. Experimental results show that, compared with the traditional detection algorithms, the proposed algorithm can extract the feature point information of the image in real time, which is of great significance to the subsequent image restoration.

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