scholarly journals An Improved Point Cloud Descriptor for Vision Based Robotic Grasping System

Sensors ◽  
2019 ◽  
Vol 19 (10) ◽  
pp. 2225 ◽  
Author(s):  
Fei Wang ◽  
Chen Liang ◽  
Changlei Ru ◽  
Hongtai Cheng
Keyword(s):  
Object Recognition ◽  
Pose Estimation ◽  
Point Cloud ◽  
Recognition Performance ◽  
Three Dimensional ◽  
Surface Shape ◽  
Feature Descriptor ◽  
Depth Sensor ◽  
Data Set ◽  
Direction Component

In this paper, a novel global point cloud descriptor is proposed for reliable object recognition and pose estimation, which can be effectively applied to robot grasping operation. The viewpoint feature histogram (VFH) is widely used in three-dimensional (3D) object recognition and pose estimation in real scene obtained by depth sensor because of its recognition performance and computational efficiency. However, when the object has a mirrored structure, it is often difficult to distinguish the mirrored poses relative to the viewpoint using VFH. In order to solve this difficulty, this study presents an improved feature descriptor named orthogonal viewpoint feature histogram (OVFH), which contains two components: a surface shape component and an improved viewpoint direction component. The improved viewpoint component is calculated by the orthogonal vector of the viewpoint direction, which is obtained based on the reference frame estimated for the entire point cloud. The evaluation of OVFH using a publicly available data set indicates that it enhances the ability to distinguish between mirrored poses while ensuring object recognition performance. The proposed method uses OVFH to recognize and register objects in the database and obtains precise poses by using the iterative closest point (ICP) algorithm. The experimental results show that the proposed approach can be effectively applied to guide the robot to grasp objects with mirrored poses.

scholarly journals Tutorial: Point Cloud Library: Three-Dimensional Object Recognition and 6 DOF Pose Estimation

2012 ◽  
Vol 19 (3) ◽  
pp. 80-91 ◽  
Author(s):  
Aitor Aldoma ◽  
Zoltan-Csaba Marton ◽  
Federico Tombari ◽  
Walter Wohlkinger ◽  
Christian Potthast ◽  
...  
Keyword(s):  
Object Recognition ◽  
Pose Estimation ◽  
Point Cloud ◽  
Three Dimensional ◽  
Dimensional Object

scholarly journals 3D Point Cloud Recognition Based on a Multi-View Convolutional Neural Network

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 3681 ◽  
Author(s):  
Le Zhang ◽  
Jian Sun ◽  
Qiang Zheng
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Point Cloud ◽  
Recognition Performance ◽  
Three Dimensional ◽  
Point Clouds ◽  
Feature Descriptor ◽  
Cloud Data ◽  
Single View ◽  
3D Point Clouds

The recognition of three-dimensional (3D) lidar (light detection and ranging) point clouds remains a significant issue in point cloud processing. Traditional point cloud recognition employs the 3D point clouds from the whole object. Nevertheless, the lidar data is a collection of two-and-a-half-dimensional (2.5D) point clouds (each 2.5D point cloud comes from a single view) obtained by scanning the object within a certain field angle by lidar. To deal with this problem, we initially propose a novel representation which expresses 3D point clouds using 2.5D point clouds from multiple views and then we generate multi-view 2.5D point cloud data based on the Point Cloud Library (PCL). Subsequently, we design an effective recognition model based on a multi-view convolutional neural network. The model directly acts on the raw 2.5D point clouds from all views and learns to get a global feature descriptor by fusing the features from all views by the view fusion network. It has been proved that our approach can achieve an excellent recognition performance without any requirement for three-dimensional reconstruction and the preprocessing of point clouds. In conclusion, this paper can effectively solve the recognition problem of lidar point clouds and provide vital practical value.

scholarly journals 3D Object Recognition and Pose Estimation From Point Cloud Using Stably Observed Point Pair Feature

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 44335-44345 ◽  
Author(s):  
Deping Li ◽  
Hanyun Wang ◽  
Ning Liu ◽  
Xiaoming Wang ◽  
Jin Xu
Keyword(s):  
Object Recognition ◽  
Pose Estimation ◽  
Point Cloud ◽  
3D Object Recognition ◽  
3D Object ◽  
Point Pair

Point Cloud Based Bin Picking: Object Recognition and Pose Estimation Using Region Growing Segmentation Algorithm

2015 ◽  
Vol 791 ◽  
pp. 189-194
Author(s):  
Frantisek Durovsky
Keyword(s):  
Object Recognition ◽  
Pose Estimation ◽  
Point Cloud ◽  
Region Growing ◽  
Segmentation Algorithm ◽  
Suction Cup ◽  
Bin Picking ◽  
Region Growing Segmentation ◽  
Dual Arm ◽  
Sensor Region

Presented paper describes experimental bin picking using Kinect sensor, region-growing algorithm, latest ROS-Industrial drivers and dual arm manipulator Motoman SDA10f.As well known if manipulation with objects of regular shapes by suction cup is required, it is sufficient to estimate only 5DoF for successful pick. In such a case simpler region growing algorithm may be used instead of complicated 3D object recognition and pose estimation techniques, resulting in shorter processing time and decrease of computational load. Experimental setup for such a scenario, manipulated objects and results using region growing segmentation algorithm are explained in detail. Video and link to open-source code of described application are provided as well.

scholarly journals CLASSIFICATION OF LIDAR DATA FOR GENERATING A HIGH-PRECISION ROADWAY MAP

2016 ◽  
Vol XLI-B3 ◽  
pp. 251-254 ◽  
Author(s):  
J. Jeong ◽  
I. Lee
Keyword(s):  
Machine Learning ◽  
Point Cloud ◽  
Three Dimensional ◽  
Autonomous Driving ◽  
Learning Objects ◽  
Support Vector ◽  
Feature Descriptor ◽  
Point Cloud Data ◽  
Cloud Data ◽  
On The Road

Generating of a highly precise map grows up with development of autonomous driving vehicles. The highly precise map includes a precision of centimetres level unlike an existing commercial map with the precision of meters level. It is important to understand road environments and make a decision for autonomous driving since a robust localization is one of the critical challenges for the autonomous driving car. The one of source data is from a Lidar because it provides highly dense point cloud data with three dimensional position, intensities and ranges from the sensor to target. In this paper, we focus on how to segment point cloud data from a Lidar on a vehicle and classify objects on the road for the highly precise map. In particular, we propose the combination with a feature descriptor and a classification algorithm in machine learning. Objects can be distinguish by geometrical features based on a surface normal of each point. To achieve correct classification using limited point cloud data sets, a Support Vector Machine algorithm in machine learning are used. Final step is to evaluate accuracies of obtained results by comparing them to reference data The results show sufficient accuracy and it will be utilized to generate a highly precise road map.

Research and Practice on New Measuring and Drafting Mode of Mechanical Drawing Based on Reverse Engineering

2012 ◽  
Vol 239-240 ◽  
pp. 645-648 ◽  
Author(s):  
Dong Yang Fang ◽  
Ai Mei Zhang ◽  
Yi Qiu
Keyword(s):  
Reverse Engineering ◽  
Point Cloud ◽  
Teaching Practice ◽  
Geometric Model ◽  
Three Dimensional ◽  
Surface Shape ◽  
Modern Engineering ◽  
Innovative Idea ◽  
Part Surface ◽  
Graphics Processing

New mode of measurement and draft in mechanical drawing based on reverse engineering is presented to reflect the idea of modern engineering design on teaching practice. Traditional measurement tools are replaced by three-dimensional scanner, whereas graphics processing is performed by using CAD technology. The processing includes three steps. Firstly, point cloud of part surface shape is obtained through scanning. Secondly, point cloud images are joined, filtered and latticed in Geomagic, which is application software of CAD. Finally, the processed point cloud is imported into Catia to reconstruct surface and three-dimensional geometric model. An innovative method of measurement and draft is accordingly proposed, which combines teaching and practices and helps to cultivate the innovative idea and abilities of students.

Learning Optimized Features for Hierarchical Models of Invariant Object Recognition

2003 ◽  
Vol 15 (7) ◽  
pp. 1559-1588 ◽  
Author(s):  
Heiko Wersing ◽  
Edgar Körner
Keyword(s):  
Object Recognition ◽  
Sparse Coding ◽  
Hierarchical Models ◽  
Feature Detection ◽  
Recognition Performance ◽  
Feature Representation ◽  
Translation Invariance ◽  
Hierarchical Network ◽  
Data Set ◽  
Invariant Object Recognition

There is an ongoing debate over the capabilities of hierarchical neural feedforward architectures for performing real-world invariant object recognition. Although a variety of hierarchical models exists, appropriate supervised and unsupervised learning methods are still an issue of intense research. We propose a feedforward model for recognition that shares components like weight sharing, pooling stages, and competitive nonlinearities with earlier approaches but focuses on new methods for learning optimal feature-detecting cells in intermediate stages of the hierarchical network. We show that principles of sparse coding, which were previously mostly applied to the initial feature detection stages, can also be employed to obtain optimized intermediate complex features. We suggest a new approach to optimize the learning of sparse features under the constraints of a weight-sharing or convolutional architecture that uses pooling operations to achieve gradual invariance in the feature hierarchy. The approach explicitly enforces symmetry constraints like translation invariance on the feature set. This leads to a dimension reduction in the search space of optimal features and allows determining more efficiently the basis representatives, which achieve a sparse decomposition of the input. We analyze the quality of the learned feature representation by investigating the recognition performance of the resulting hierarchical network on object and face databases. We show that a hierarchy with features learned on a single object data set can also be applied to face recognition without parameter changes and is competitive with other recent machine learning recognition approaches. To investigate the effect of the interplay between sparse coding and processing nonlinearities, we also consider alternative feedforward pooling nonlinearities such as presynaptic maximum selection and sum-of-squares integration. The comparison shows that a combination of strong competitive nonlinearities with sparse coding offers the best recognition performance in the difficult scenario of segmentation-free recognition in cluttered surround. We demonstrate that for both learning and recognition, a precise segmentation of the objects is not necessary.

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