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Robotics and 3D Computer Vision

A topical collection in Sensors (ISSN 1424-8220). This collection belongs to the section "Sensing and Imaging".

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Editors

Faculty of Electrical Engineering, Computer Science and Information Technology Osijek, Josip Juraj Strossmayer University of Osijek, Kneza Trpimira 2B, 31000 Osijek, Croatia
Interests: robot vision; soft computing methods; machine learning methods
Faculty of Electrical Engineering, Computer Science and Information Technology Osijek, Josip Juraj Strossmayer University of Osijek, Kneza Trpimira 2B, 31000 Osijek, Croatia
Interests: robotics; artificial intelligence; soft computing; machine learning; optimization algorithms; medical applications; agriculture applications
Faculty of Electrical Engineering, Computer Science and Information Technology Osijek, Josip Juraj Strossmayer University of Osijek, Kneza Trpimira 2B, 31000 Osijek, Croatia
Interests: machine vision; machine learning; robotics
Biomimetics and Intelligent Systems Group, University of Oulu, P.O. BOX 4500, FI-90014 Oulu, Finland
Interests: computer vision; robotics (especially mobile robots); intelligent signal analysis; software security

Topical Collection Information

Dear Colleagues,

With the appearance of the low-cost Microsoft Kinect sensor about a decade ago, followed by other time-of-flight and structured light sensors with affordable prices, research in the field of 3D computer vision exploded overnight. Prior to this, real-time 3D vision was mainly performed using stereo vision and Lidar sensors. Previous stereo vision with software-based image processing was outlier-prone, especially when homogenous surfaces were involved. Laser-based Lidar, on the other hand, is amongst the most accurate of the three groups of sensors but this type of sensor is still relatively expensive. Recent advancements in stereo vision algorithms and hardware have resulted in fast and accurate setereo vision sensors with hardware-based image processing.

The current rapid innovation in robotics is driven by 3D vision capabilities. For mobile robots, and as industrial robots to successfully work in unstructured environments, accurate 3D scene reconstruction and understanding as well as localization capabilities are required.

This Topical Collection aims to cover different aspects of the recent advances of 3D vision, especially in the field of robotics. Topics of interest include (but are not limited to):

  • 3D scene reconstruction and understanding;
  • Localization;
  • 3D object recognition and representation;
  • Applications of 3D vision in various field, e.g., medicine, agriculture,industry, automotive, etc.

Dr. Emmanuel Karlo Nyarko
Dr. Damir Filko
Prof. Dr. Robert Cupec
Dr. Juha Röning
Collection Editors

Manuscript Submission Information

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Keywords

  • 3D Vision
  • Robot vision
  • Robotics
  • 3D scene reconstruction
  • 3D object recognition

Published Papers (5 papers)

2023

Jump to: 2022, 2021

16 pages, 9064 KiB  
Article
Accurate Monocular SLAM Initialization via Structural Line Tracking
by Tianlun Gu, Jianwei Zhang and Yanli Liu
Sensors 2023, 23(24), 9870; https://doi.org/10.3390/s23249870 - 16 Dec 2023
Viewed by 717
Abstract
In this paper, we present a novel monocular simultaneous localization and mapping (SLAM) initialization algorithm that relies on structural features by tracking structural lines. This approach addresses the limitations of the traditional method, which can fail to account for a lack of features [...] Read more.
In this paper, we present a novel monocular simultaneous localization and mapping (SLAM) initialization algorithm that relies on structural features by tracking structural lines. This approach addresses the limitations of the traditional method, which can fail to account for a lack of features or their uneven distribution. Our proposed method utilizes a sliding window approach to guarantee the quality and stability of the initial pose estimation. We incorporate multiple geometric constraints, orthogonal dominant directions, and coplanar structural lines to construct an efficient pose optimization strategy. Experimental evaluations conducted on both the collected chessboard datasets and real scene datasets show that our approach provides superior results in terms of accuracy and real-time performance compared to the well-tuned baseline methods. Notably, our algorithm achieves these improvements while being computationally lightweight, without the need for matrix decomposition. Full article
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2022

Jump to: 2023, 2021

14 pages, 3258 KiB  
Article
MVS-T: A Coarse-to-Fine Multi-View Stereo Network with Transformer for Low-Resolution Images 3D Reconstruction
by Ruiming Jia, Xin Chen, Jiali Cui and Zhenghui Hu
Sensors 2022, 22(19), 7659; https://doi.org/10.3390/s22197659 - 09 Oct 2022
Cited by 3 | Viewed by 1809
Abstract
A coarse-to-fine multi-view stereo network with Transformer (MVS-T) is proposed to solve the problems of sparse point clouds and low accuracy in reconstructing 3D scenes from low-resolution multi-view images. The network uses a coarse-to-fine strategy to estimate the depth of the image progressively [...] Read more.
A coarse-to-fine multi-view stereo network with Transformer (MVS-T) is proposed to solve the problems of sparse point clouds and low accuracy in reconstructing 3D scenes from low-resolution multi-view images. The network uses a coarse-to-fine strategy to estimate the depth of the image progressively and reconstruct the 3D point cloud. First, pyramids of image features are constructed to transfer the semantic and spatial information among features at different scales. Then, the Transformer module is employed to aggregate the image’s global context information and capture the internal correlation of the feature map. Finally, the image depth is inferred by constructing a cost volume and iterating through the various stages. For 3D reconstruction of low-resolution images, experiment results show that the 3D point cloud obtained by the network is more accurate and complete, which outperforms other advanced algorithms in terms of objective metrics and subjective visualization. Full article
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18 pages, 6143 KiB  
Article
Sym3DNet: Symmetric 3D Prior Network for Single-View 3D Reconstruction
by Ashraf Siddique and Seungkyu Lee
Sensors 2022, 22(2), 518; https://doi.org/10.3390/s22020518 - 11 Jan 2022
Cited by 4 | Viewed by 2388
Abstract
The three-dimensional (3D) symmetry shape plays a critical role in the reconstruction and recognition of 3D objects under occlusion or partial viewpoint observation. Symmetry structure prior is particularly useful in recovering missing or unseen parts of an object. In this work, we propose [...] Read more.
The three-dimensional (3D) symmetry shape plays a critical role in the reconstruction and recognition of 3D objects under occlusion or partial viewpoint observation. Symmetry structure prior is particularly useful in recovering missing or unseen parts of an object. In this work, we propose Sym3DNet for single-view 3D reconstruction, which employs a three-dimensional reflection symmetry structure prior of an object. More specifically, Sym3DNet includes 2D-to-3D encoder-decoder networks followed by a symmetry fusion step and multi-level perceptual loss. The symmetry fusion step builds flipped and overlapped 3D shapes that are fed to a 3D shape encoder to calculate the multi-level perceptual loss. Perceptual loss calculated in different feature spaces counts on not only voxel-wise shape symmetry but also on the overall global symmetry shape of an object. Experimental evaluations are conducted on both large-scale synthetic 3D data (ShapeNet) and real-world 3D data (Pix3D). The proposed method outperforms state-of-the-art approaches in terms of efficiency and accuracy on both synthetic and real-world datasets. To demonstrate the generalization ability of our approach, we conduct an experiment with unseen category samples of ShapeNet, exhibiting promising reconstruction results as well. Full article
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17 pages, 5002 KiB  
Article
Pose Determination of the Disc Cutter Holder of Shield Machine Based on Monocular Vision
by Dandan Peng, Guoli Zhu, Dailin Zhang, Zhe Xie, Rui Liu, Jinlong Hu and Yang Liu
Sensors 2022, 22(2), 467; https://doi.org/10.3390/s22020467 - 08 Jan 2022
Cited by 3 | Viewed by 1401
Abstract
The visual measurement system plays a vital role in the disc cutter changing robot of the shield machine, and its accuracy directly determines the success rate of the disc cutter grasping. However, the actual industrial environment with strong noise brings a great challenge [...] Read more.
The visual measurement system plays a vital role in the disc cutter changing robot of the shield machine, and its accuracy directly determines the success rate of the disc cutter grasping. However, the actual industrial environment with strong noise brings a great challenge to the pose measurement methods. The existing methods are difficult to meet the required accuracy of pose measurement based on machine vision under the disc cutter changing conditions. To solve this problem, we propose a monocular visual pose measurement method consisting of the high precision optimal solution to the PnP problem (OPnP) method and the highly robust distance matching (DM) method. First, the OPnP method is used to calculate the rough pose of the shield machine’s cutter holder, and then the DM method is used to measure its pose accurately. Simulation results show that the proposed monocular measurement method has better accuracy and robustness than the several mainstream PnP methods. The experimental results also show that the maximum error of the proposed method is 0.28° in the direction of rotation and 0.32 mm in the direction of translation, which can meet the measurement accuracy requirement of the vision system of the disc cutter changing robot in practical engineering application. Full article
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2021

Jump to: 2023, 2022

20 pages, 5826 KiB  
Article
A Coarse-to-Fine Method for Estimating the Axis Pose Based on 3D Point Clouds in Robotic Cylindrical Shaft-in-Hole Assembly
by Can Li, Ping Chen, Xin Xu, Xinyu Wang and Aijun Yin
Sensors 2021, 21(12), 4064; https://doi.org/10.3390/s21124064 - 12 Jun 2021
Cited by 10 | Viewed by 2893
Abstract
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 [...] Read more.
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. Full article
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