A Fast Point Clouds Registration Algorithm for Laser Scanners

Xu, Guangxuan; Pang, Yajun; Bai, Zhenxu; Wang, Yulei; Lü, Zhiwei

doi:10.3390/app11083426

Cited by 38 publications

(28 citation statements)

References 21 publications

(22 reference statements)

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“…The use of low-cost solutions in the form of sensors to measure linear displacements can help enhance the results’ reliability and allow the FEM model to be verified. Based on the alignment of the data, we applied the ICP algorithm [ 48 , 49 , 50 , 51 , 52 ] and evenly distributed the targets, which were treated as references [ 53 , 54 , 55 ]. Given that the bridge structure consisted of two spans, the scanner position was not at the same point during the tests.…”

Section: Experiments Results—comparison Of Measurementsmentioning

confidence: 99%

Bridge Non-Destructive Measurements Using a Laser Scanning during Acceptance Testing: Case Study

2022

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Owing to the recent proliferation of inventory works on roads and railways, bridge acceptance tests have increased exponentially. These tests’ results are often misinterpreted owing to the use of various measuring equipment types, rendering integrated interpretation problematic. It is also problematic that adjusting the measurement method is difficult when the structure’s response to load is uncertain. Therefore, it is important to observe the largest possible range of possible deformations. For this reason, the present study suggests a novel approach to bridge non-destructive measurements using a laser scanner during acceptance testing. The main advantage of our method is the ability it affords to observe all points of the structure during testing, an ability that is extremely important is the absence of unambiguous data regarding the bridge’s condition. To significantly increase the scanning accuracy (up to 0.5 mm), measurements from a limited number of linear sensors are used (whose accuracy is up to 0.1 mm). To achieve optimal accuracy, we performed the following steps: first, we adapted the precision requirements to the numerical project. For this purpose, we used potentiometric sensors to measure linear deformations. Next, we performed laser scanning measurements from two scan positions. Finally, we filtered the data for the selected cross-section and modelled the points into polynomial deflection. The performed tests confirmed that the structure’s response was as predicted by the FEM model, and the object was approved for use. Our future tests will be based on the selection of a structure with minimal measurement errors, and the results will be compared using a total station, ensuring the highest possible quality of service, which can be repeated in simple steps. As study objects, we presented two items: the first without proper calibration on a linear sensor and the second using linear sensors to present the highest possible accuracy of our experiment.

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Section: Experiments Results—comparison Of Measurementsmentioning

confidence: 99%

Bridge Non-Destructive Measurements Using a Laser Scanning during Acceptance Testing: Case Study

2022

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“…Therefore, the points with obvious geometric features are selected from the target point cloud Q to form the key point set Q t , and only the features of the key points are extracted, which can significantly improve the efficiency of feature extraction of the point cloud. Due to the advantages of high speed, accuracy and robustness, an intrinsic Shape Signature (ISS) algorithm is developed to extract key points, and it is suitable for various applications [ 31 ]. The main steps are as follows: (1) Establish a local coordinate at each point q v in point cloud Q and set a neighborhood search radius r f .…”

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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“…Selecting key points to simplify the point clouds can retain the features of the point clouds as much as possible while reducing the number of the points. The Intrinsic Shape Signatures (ISS) is a widely used algorithm with a fast calculation speed and high repeatability to realize key point extraction [ 27 ]. The extraction procedures of the key points PF i are summarized as follows:…”

Section: The Proposed Methodsmentioning

confidence: 99%

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

Shao

Fan

Zhu

et al. 2022

Sensors

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Automated guided vehicles are widely used in warehousing environments for automated pallet handling, which is one of the fundamental parts to construct intelligent logistics systems. Pallet detection is a critical technology for automated guided vehicles, which directly affects production efficiency. A novel pallet detection method for automated guided vehicles based on point cloud data is proposed, which consists of five modules including point cloud preprocessing, key point extraction, feature description, surface matching and point cloud registration. The proposed method combines the color with the geometric features of the pallet point cloud and constructs a new Adaptive Color Fast Point Feature Histogram (ACFPFH) feature descriptor by selecting the optimal neighborhood adaptively. In addition, a new surface matching method called the Bidirectional Nearest Neighbor Distance Ratio-Approximate Congruent Triangle Neighborhood (BNNDR-ACTN) is proposed. The proposed method overcomes the problems of current methods such as low efficiency, poor robustness, random parameter selection, and being time-consuming. To verify the performance, the proposed method is compared with the traditional and modified Iterative Closest Point (ICP) methods in two real-world cases. The results show that the Root Mean Square Error (RMSE) is reduced to 0.009 and the running time is reduced to 0.989 s, which demonstrates that the proposed method has faster registration speed while maintaining higher registration accuracy.

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A Fast Point Clouds Registration Algorithm for Laser Scanners

Cited by 38 publications

References 21 publications

Bridge Non-Destructive Measurements Using a Laser Scanning during Acceptance Testing: Case Study

Bridge Non-Destructive Measurements Using a Laser Scanning during Acceptance Testing: Case Study

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

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

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