A Dense RGB-D SLAM Algorithm based on Convolutional Neural Network of Multi-layer Image Invariant Feature

2021 ◽  
Author(s):  
SU YAN ◽  
Lei Yu
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Pose Estimation ◽  
Autonomous Vehicles ◽  
Reconstruction Algorithm ◽  
Point Clouds ◽  
Feature Point ◽  
3D Point Clouds ◽  
Invariant Feature ◽  
Layer Image

Abstract Simultaneous Localization and Mapping (SLAM) is one of the key technologies used in sweepers, autonomous vehicles, virtual reality and other fields. This paper presents a dense RGB-D SLAM reconstruction algorithm based on convolutional neural network of multi-layer image invariant feature transformation. The main contribution of the system lies in the construction of a convolutional neural network based on multi-layer image invariant feature, which optimized the extraction of ORB (Oriented FAST and Rotated Brief) feature points and the reconstruction effect. After the feature point matching, pose estimation, loop detection and other steps, the 3D point clouds were finally spliced to construct a complete and smooth spatial model. The system can improve the accuracy and robustness in feature point processing and pose estimation. Comparative experiments show that the optimized algorithm saves 0.093s compared to the ordinary extraction algorithm while guaranteeing a high accuracy rate at the same time. The results of reconstruction experiments show that the spatial models have more clear details, smoother connection with no fault layers than the original ones. The reconstruction results are generally better than other common algorithms, such as Kintinuous, Elasticfusion and ORBSLAM2 dense reconstruction.

scholarly journals Convolutional Neural Network for Extracting 3D Point Clouds of Fibrous Web From Multi-Focus Images

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 87857-87869
Author(s):  
Jue Hou ◽  
Wenbin Ouyang ◽  
Bugao Xu ◽  
Rongwu Wang
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Point Clouds ◽  
3D Point Clouds

Convolutional neural network for 3D point clouds matching

2021 ◽  
Author(s):  
Sergei Voronin ◽  
Artyom Makovetskii ◽  
Aleksei Voronin ◽  
Dmitrii Zhernov
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Point Clouds ◽  
3D Point Clouds

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 A Coarse-to-Fine Method for Estimating the Axis Pose Based on 3D Point Clouds in Robotic Cylindrical Shaft-in-Hole Assembly

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 4064
Author(s):  
Can Li ◽  
Ping Chen ◽  
Xin Xu ◽  
Xinyu Wang ◽  
Aijun Yin
Keyword(s):  
Pose Estimation ◽  
Point Clouds ◽  
Geometric Constraints ◽  
Admittance Control ◽  
3D Vision ◽  
Axis Orientation ◽  
3D Point Clouds ◽  
Object Pose Estimation ◽  
Traditional Approaches ◽  
Coarse To Fine

In this work, we propose a novel coarse-to-fine method for object pose estimation coupled with admittance control to promote robotic shaft-in-hole assembly. Considering that traditional approaches to locate the hole by force sensing are time-consuming, we employ 3D vision to estimate the axis pose of the hole. Thus, robots can locate the target hole in both position and orientation and enable the shaft to move into the hole along the axis orientation. In our method, first, the raw point cloud of a hole is processed to acquire the keypoints. Then, a coarse axis is extracted according to the geometric constraints between the surface normals and axis. Lastly, axis refinement is performed on the coarse axis to achieve higher precision. Practical experiments verified the effectiveness of the axis pose estimation. The assembly strategy composed of axis pose estimation and admittance control was effectively applied to the robotic shaft-in-hole assembly.

Real-Time 3D object detection using improved convolutional neural network based on image-driven point cloud

2021 ◽  
Vol 14 ◽  
Author(s):  
Zhiyong Gao ◽  
Jianhong Xiang
Keyword(s):  
Neural Network ◽  
Object Detection ◽  
Convolutional Neural Network ◽  
Real Time ◽  
Point Cloud ◽  
Point Clouds ◽  
3D Point Cloud ◽  
3D Object ◽  
3D Object Detection ◽  
Instance Segmentation

Background: While detecting the object directly from the 3D point cloud, the natural 3D patterns and invariance of 3D data are often obscure. Objective: In this work, we aimed at studying the 3D object detection from discrete, disordered and sparse 3D point clouds. Methods: The CNN is composed of the frustum sequence module, 3D instance segmentation module S-NET, 3D point cloud transformation module T-NET, and 3D boundary box estimation module E-NET. The search space of the object is determined by the frustum sequence module. The instance segmentation of the point cloud is performed by the 3D instance segmentation module. The 3D coordinates of the object are confirmed by the transformation module and the 3D bounding box estimation module. Results: Evaluated on KITTI benchmark dataset, our method outperforms the state of the art by remarkable margins while having real-time capability. Conclusion: We achieve real-time 3D object detection by proposing an improved convolutional neural network (CNN) based on image-driven point clouds.

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