A Novel Pallet Detection Method for Automated Guided Vehicles Based on Point Cloud Data

Shao, Yiping; Fan, Zhengshuai; Zhu, Baochang; Zhou, Minlong; Chen, Zhihui; Lu, Jiangfeng

doi:10.3390/s22208019

Cited by 6 publications

(3 citation statements)

References 31 publications

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“…The detailed steps are as follows: Set the range of point cloud neighborhood search radius r j from lower limit r min to upper limit r max with radius interval r d . The upper and lower limits of the radius range are determined by the average point cloud distance d p , which is defined as follows [ 32 ]:

where N represents the total number of points in the pallet point cloud, and d m represents the distance of each key point q k from its nearest point. Set

, where

.…”

Section: The Proposed Pallet Pose Estimation Methodsmentioning

confidence: 99%

A Point Cloud Data-Driven Pallet Pose Estimation Method Using an Active Binocular Vision Sensor

Shao

Fan

Zhu

et al. 2023

Sensors

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Pallet pose estimation is one of the key technologies for automated fork pickup of driverless industrial trucks. Due to the complex working environment and the enormous amount of data, the existing pose estimation approaches cannot meet the working requirements of intelligent logistics equipment in terms of high accuracy and real time. A point cloud data-driven pallet pose estimation method using an active binocular vision sensor is proposed, which consists of point cloud preprocessing, Adaptive Gaussian Weight-based Fast Point Feature Histogram extraction and point cloud registration. The proposed method overcomes the shortcomings of traditional pose estimation methods, such as poor robustness, time consumption and low accuracy, and realizes the efficient and accurate estimation of pallet pose for driverless industrial trucks. Compared with traditional Fast Point Feature Histogram and Signature of Histogram of Orientation, the experimental results show that the proposed approach is superior to the above two methods, improving the accuracy by over 35% and reducing the feature extraction time by over 30%, thereby verifying the effectiveness and superiority of the proposed method.

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where N represents the total number of points in the pallet point cloud, and d m represents the distance of each key point q k from its nearest point. Set

, where

.…”

Section: The Proposed Pallet Pose Estimation Methodsmentioning

confidence: 99%

A Point Cloud Data-Driven Pallet Pose Estimation Method Using an Active Binocular Vision Sensor

Shao

Fan

Zhu

et al. 2023

Sensors

Self Cite

View full text Add to dashboard Cite

show abstract

“…Point cloud classification has been active in the research fields such as photogrammetry and remote sensing for decades. It has become an important part of intelligent vehicles [1], automatic driving [2], 3D reconstruction [3], forest monitoring [4], robot perception [5], traffic signage extraction [6] and so on. Due to factors like irregularity and disorder, high sensor noise, complex scenes and non-homogeneous density, point cloud classification is still challenging.…”

Section: Introductionmentioning

confidence: 99%

Point-MDA: Progressive Point Cloud Analysis for Extreme Multi-Scale Detail Activation

Li¹,

Zhao²,

Zhou³

et al. 2023

Preprint

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The point cloud is a form of three-dimensional data that comprises various detailed features at multiple scales. Due to this characteristic and its irregularity, point cloud analysis based on deep learning is challenging. While previous works utilize the sampling-grouping operation of PointNet++ for feature description and then explore geometry by means of sophisticated feature extractors or deep networks, such operations fail to describe multi-scale features effectively. Additionally, these techniques have led to performance saturation. And it is difficult for standard MLPs to directly "mine" point cloud. To address above problems, we propose the Detail Activation (DA) module, which encodes data based on Fourier transform after sampling and grouping. We activate the channels at different frequency levels from low to high in the DA module to gradually recover finer point cloud details. As training progresses, the proposed Point-MDA can uncover local and global geometries of point cloud progressively. Our experiments show that Point-MDA achieves superior classification accuracy, outperforming PointNet++ by 3.3% and 7.9% in terms of overall accuracy on the ModelNet40 and ScanObjectNN dataset, respectively. Furthermore, it accomplishes this without employing complicated operations, while exploring the full potential of PointNet++.

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“…Shao et al used an RGB-D camera to detect euro pallet objects, performing pallet detection from point cloud data. They proposed a new method by combining color and geometric shape data [13]. Upon reviewing the literature, it is observed that mobile robots are being increasingly employed for in-factory transportation and are still under development.…”

Section: Introductionmentioning

confidence: 99%

EuroPallet Detection with RGB-D Camera Based on Deep Learning

Kirci,

Atali

2023

EJRnD

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This study related to enhancing processes and production efficiency in industrial settings by opting for autonomous mobile robots dedicated to indoor transportation and logistics. The specific focus of this research is on employing deep learning for the detection of Euro pallet objects, enabling a mobile robot, specifically the Servant-T1500 model by Kar Metal company with natural navigation capabilities, to autonomously dock and handle palletized loads with precision. To accomplish this, an original dataset was curated using an RGB-D camera, and data augmentation techniques were applied to expand this dataset. Subsequently, a deep learning model was trained on this data to detect Euro pallets in images it had not encountered during training. The transfer learning method was applied using the YOLOv5 model on the dataset. The successful outcome of this process demonstrates the autonomous robot's capability to accurately recognize Euro pallet objects even under changing lighting conditions. This achievement marks a significant step toward optimizing industrial processes and improving production efficiency through the integration of autonomous mobile robots in logistics and transportation tasks.

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A Novel Pallet Detection Method for Automated Guided Vehicles Based on Point Cloud Data

Cited by 6 publications

References 31 publications

A Point Cloud Data-Driven Pallet Pose Estimation Method Using an Active Binocular Vision Sensor

A Point Cloud Data-Driven Pallet Pose Estimation Method Using an Active Binocular Vision Sensor

Point-MDA: Progressive Point Cloud Analysis for Extreme Multi-Scale Detail Activation

EuroPallet Detection with RGB-D Camera Based on Deep Learning

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