GNSS-IMU-assisted colored ICP for UAV-LiDAR point cloud registration of peach trees

Yuan, Wenan; Choi, Daeun; Bolkas, Dimitrios

doi:10.1016/j.compag.2022.106966

Cited by 23 publications

(15 citation statements)

References 52 publications

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“…Te use of point cloud normal can provide a better initial position for the matching point cloud and improve the accuracy and efciency of the registration [48][49][50]. In this paper, the kd-tree algorithm is used to achieve fast retrieval of the nearest neighbors [51,52]. When a set of neighborhood points is found and denoted…”

Section: Estimation Of the Normal Vector Of The Target Planementioning

confidence: 99%

Fast Extraction Algorithm of Planar Targets Based on Point Cloud Data for Monitoring the Synchronization of Bridge Jacking Displacements

Liang,

Zhang,

Zhang

et al. 2024

Structural Control and Health Monitoring

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Transverse synchronization of vertical displacements of all jacking-up points is an important monitoring indicator to replace bearings in assembled multigirder bridges during the jacking phase. Currently, using target paper to identify the 3D coordinates of control points reduces the complexity of monitoring operations and improves the stability of data precision. However, the existing planar target locating methods have low accuracy, inefficiency, and subjectivity, which seriously hinders the construction process of bearing replacement. Accurately obtaining the center coordinates of multiple targets from a point cloud in a short monitoring period remains a challenge. This study proposes a high-precision automated algorithm to extract target center points in low-density point clouds to quickly calculate real target center points. First, we construct a standard point cloud model of the target papers for scanning, including color and geometric features. Then, we extract the measured point cloud of the typical jacking operation phase based on the reflection intensity and size information. Next, we map the intensity values of the measured point cloud into the color channel and register the measured point cloud with its standard point cloud model using the normal vector estimation and colored ICP algorithms. Finally, we extract the center point of the measured targets. Numerical experiments and engineering test results show that the proposed method converges quickly with high precision and good robustness, which saves 91.4% of the time compared with the traditional method. In general, this research can provide effective technical support for 3D laser scanning in monitoring the operation phase of bridge jacking.

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Section: Estimation Of the Normal Vector Of The Target Planementioning

confidence: 99%

Fast Extraction Algorithm of Planar Targets Based on Point Cloud Data for Monitoring the Synchronization of Bridge Jacking Displacements

Liang,

Zhang,

Zhang

et al. 2024

Structural Control and Health Monitoring

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“…Another notable development in multi-constellation receivers is the integration of additional sensors to complement the GNSS positioning information. Inertial Measurement Units (IMUs) are commonly integrated into these receivers to provide measurements of acceleration and angular rates [45,46]. The combination of GNSS and IMU data enables the estimation of attitude, velocity, and position with higher accuracy and improved robustness.…”

Section: Global Navigation Satellite Systemsmentioning

confidence: 99%

Global Navigation Satellite Systems as State-of-the-Art Solutions in Precision Agriculture: A Review of Studies Indexed in the Web of Science

2023

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Global Navigation Satellite Systems (GNSS) in precision agriculture (PA) represent a cornerstone for field mapping, machinery guidance, and variable rate technology. However, recent improvements in GNSS components (GPS, GLONASS, Galileo, and BeiDou) and novel remote sensing and computer processing-based solutions in PA have not been comprehensively analyzed in scientific reviews. Therefore, this study aims to explore novelties in GNSS components with an interest in PA based on the analysis of scientific papers indexed in the Web of Science Core Collection (WoSCC). The novel solutions in PA using GNSS were determined and ranked based on the citation topic micro criteria in the WoSCC. The most represented citation topics micro based on remote sensing were “NDVI”, “LiDAR”, “Harvesting robot”, and “Unmanned aerial vehicles” while the computer processing-based novelties included “Geostatistics”, “Precise point positioning”, “Simultaneous localization and mapping”, “Internet of things”, and “Deep learning”. Precise point positioning, simultaneous localization and mapping, and geostatistics were the topics that most directly relied on GNSS in 93.6%, 60.0%, and 44.7% of the studies indexed in the WoSCC, respectively. Meanwhile, harvesting robot research has grown rapidly in the past few years and includes several state-of-the-art sensors, which can be expected to improve further in the near future.

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“…In the past, in lidar SLAM studied the focus was on environments such as autonomous driving and construction sites [23,26,27]. The objects detected were usually man-made objects with simple 3D constructions, and plants with complex constructions were relatively less discussed [23,[28][29][30][31][32][33][34][35]. Among them, the iterative closest point (ICP) method, which mainly uses the 3D direct matching method, has been used to perform relevant studies on the structure of tree crowns, diameter at breast height, and plant parameters such as tree height.…”

Section: Introductionmentioning

confidence: 99%

Accuracy Evaluation and Branch Detection Method of 3D Modeling Using Backpack 3D Lidar SLAM and UAV-SfM for Peach Trees during the Pruning Period in Winter

et al. 2023

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In the winter pruning operation of deciduous fruit trees, the number of pruning branches and the structure of the main branches greatly influence the future growth of the fruit trees and the final harvest volume. Terrestrial laser scanning (TLS) is considered a feasible method for the 3D modeling of trees, but it is not suitable for large-scale inspection. The simultaneous localization and mapping (SLAM) technique makes it possible to move the lidar on the ground and model quickly, but it is not useful enough for the accuracy of plant detection. Therefore, in this study, we used UAV-SfM and 3D lidar SLAM techniques to build 3D models for the winter pruning of peach trees. Then, we compared and analyzed these models and further proposed a method to distinguish branches from 3D point clouds by spatial point cloud density. The results showed that the 3D lidar SLAM technique had a shorter modeling time and higher accuracy than UAV-SfM for the winter pruning period of peach trees. The method had the smallest RMSE of 3084 g with an R2 = 0.93 compared to the fresh weight of the pruned branches. In the branch detection part, branches with diameters greater than 3 cm were differentiated successfully, regardless of whether before or after pruning.

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GNSS-IMU-assisted colored ICP for UAV-LiDAR point cloud registration of peach trees

Cited by 23 publications

References 52 publications

Fast Extraction Algorithm of Planar Targets Based on Point Cloud Data for Monitoring the Synchronization of Bridge Jacking Displacements

Fast Extraction Algorithm of Planar Targets Based on Point Cloud Data for Monitoring the Synchronization of Bridge Jacking Displacements

Global Navigation Satellite Systems as State-of-the-Art Solutions in Precision Agriculture: A Review of Studies Indexed in the Web of Science

Accuracy Evaluation and Branch Detection Method of 3D Modeling Using Backpack 3D Lidar SLAM and UAV-SfM for Peach Trees during the Pruning Period in Winter

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