Roof bolt identification in underground coal mines from 3D point cloud data using local point descriptors and artificial neural network

2020 ◽  
Vol 42 (1) ◽  
pp. 367-377
Author(s):  
Sarvesh Kumar Singh ◽  
Simit Raval ◽  
Bikram Banerjee
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Point Cloud ◽  
Coal Mines ◽  
3D Point Cloud ◽  
Point Cloud Data ◽  
Roof Bolt ◽  
Cloud Data ◽  
Local Point ◽  
Underground Coal Mines

scholarly journals Learning Collision Situation to Convolutional Neural Network Using Collision Grid Map Based on Probability Scheme

2020 ◽  
Vol 10 (2) ◽  
pp. 617
Author(s):  
Jo ◽  
Moon
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Point Cloud ◽  
Probability Method ◽  
3D Point Cloud ◽  
Point Cloud Data ◽  
Cloud Data ◽  
Location Data ◽  
Grid Map ◽  
Permutation Problem

In this paper, a Collision Grid Map (CGM) is proposed by using 3d point cloud data to predict the collision between the cattle and the end effector of the manipulator in the barn environment. The Generated Collision Grid Map using x-y plane and depth z data in 3D point cloud data is applied to a Convolutional Neural Network to predict a collision situation. There is an invariant of the permutation problem, which is not efficiently learned in occurring matter of different orders when 3d point cloud data is applied to Convolutional Neural Network. The Collision Grid Map is generated by point cloud data based on the probability method. The Collision Grid Map scheme is composed of a 2-channel. The first channel is constructed by location data in the x-y plane. The second channel is composed of depth data in the z-direction. 3D point cloud is measured in a barn environment and created a Collision Grid Map. Then the generated Collision Grid Map is applied to the Convolutional Neural Network to predict the collision with cattle. The experimental results show that the proposed scheme is reliable and robust in a barn environment.

Fast Intent Prediction of Multi-Cyclists in 3D Point Cloud Data using Deep Neural Networks

2021 ◽  
Author(s):  
Khaled Saleh ◽  
Ahmed Abobakr ◽  
Mohammed Hossny ◽  
Darius Nahavandi ◽  
Julie Iskander ◽  
...  
Keyword(s):  
Neural Networks ◽  
Point Cloud ◽  
Deep Neural Networks ◽  
3D Point Cloud ◽  
Point Cloud Data ◽  
Cloud Data ◽  
Intent Prediction

Automated Surface Inspection Using 3D Point Cloud Data in Manufacturing: A Case Study

2018 ◽  
Author(s):  
Romina Dastoorian ◽  
Ahmad E. Elhabashy ◽  
Wenmeng Tian ◽  
Lee J. Wells ◽  
Jaime A. Camelio
Keyword(s):  
Point Cloud ◽  
Surface Defect ◽  
Fault Location ◽  
Three Dimensional ◽  
Practical Case ◽  
3D Point Cloud ◽  
Point Cloud Data ◽  
Defect Inspection ◽  
Cloud Data

With the latest advancements in three-dimensional (3D) measurement technologies, obtaining 3D point cloud data for inspection purposes in manufacturing is becoming more common. While 3D point cloud data allows for better inspection capabilities, their analysis is typically challenging. Especially with unstructured 3D point cloud data, containing coordinates at random locations, the challenges increase with higher levels of noise and larger volumes of data. Hence, the objective of this paper is to extend the previously developed Adaptive Generalized Likelihood Ratio (AGLR) approach to handle unstructured 3D point cloud data used for automated surface defect inspection in manufacturing. More specifically, the AGLR approach was implemented in a practical case study to inspect twenty-seven samples, each with a unique fault. These faults were designed to cover an array of possible faults having three different sizes, three different magnitudes, and located in three different locations. The results show that the AGLR approach can indeed differentiate between non-faulty and a varying range of faulty surfaces while being able to pinpoint the fault location. This work also serves as a validation for the previously developed AGLR approach in a practical scenario.

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