scholarly journals CMPC: An Innovative Lidar-Based Method to Estimate Tree Canopy Meshing-Profile Volumes for Orchard Target-Oriented Spray

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 4252
Author(s):  
Chenchen Gu ◽  
Changyuan Zhai ◽  
Xiu Wang ◽  
Songlin Wang
Keyword(s):  
Point Cloud ◽  
Tree Canopy ◽  
Point Cloud Data ◽  
Cross Sectional ◽  
Cloud Data ◽  
Spray System ◽  
Canopy Volume ◽  
High Degree ◽  
Degree Of Similarity ◽  
Moving Speed

Canopy characterization detection is essential for target-oriented spray, which minimizes pesticide residues in fruits, pesticide wastage, and pollution. In this study, a novel canopy meshing-profile characterization (CMPC) method based on light detection and ranging (LiDAR)point-cloud data was designed for high-precision canopy volume calculations. First, the accuracy and viability of this method were tested using a simulated canopy. The results show that the CMPC method can accurately characterize the 3D profiles of the simulated canopy. These simulated canopy profiles were similar to those obtained from manual measurements, and the measured canopy volume achieved an accuracy of 93.3%. Second, the feasibility of the method was verified by a field experiment where the canopy 3D stereogram and cross-sectional profiles were obtained via CMPC. The results show that the 3D stereogram exhibited a high degree of similarity with the tree canopy, although there were some differences at the edges, where the canopy was sparse. The CMPC-derived cross-sectional profiles matched the manually measured results well. The CMPC method achieved an accuracy of 96.3% when the tree canopy was detected by LiDAR at a moving speed of 1.2 m/s. The accuracy of the LiDAR system was virtually unchanged when the moving speeds was reduced to 1 m/s. No detection lag was observed when comparing the start and end positions of the cross-section. Different CMPC grid sizes were also evaluated. Small grid sizes (0.01 m × 0.01 m and 0.025 m × 0.025 m) were suitable for characterizing the finer details of a canopy, whereas grid sizes of 0.1 m × 0.1 m or larger can be used for characterizing its overall profile and volume. The results of this study can be used as a technical reference for the development of a LiDAR-based target-oriented spray system.

scholarly journals A Deformable Configuration Planning Framework for a Parallel Wheel-Legged Robot Equipped with Lidar

Sensors ◽  
2020 ◽  
Vol 20 (19) ◽  
pp. 5614
Author(s):  
Fei Guo ◽  
Shoukun Wang ◽  
Binkai Yue ◽  
Junzheng Wang
Keyword(s):  
Point Cloud ◽  
Optimization Method ◽  
Mapping Method ◽  
Point Cloud Data ◽  
Confined Space ◽  
Cloud Data ◽  
Planning Framework ◽  
Gradient Optimization ◽  
High Degree ◽  
Fusion Approach

The wheel-legged hybrid robot (WLHR) is capable of adapting height and wheelbase configuration to traverse obstacles or rolling in confined space. Compared with legged and wheeled machines, it can be applied for more challenging mobile robotic exercises using the enhanced environment adapting performance. To make full use of the deformability and traversability of WHLR with parallel Stewart mechanism, this paper presents an optimization-driven planning framework for WHLR with parallel Stewart mechanism by abstracting the robot as a deformable bounding box. It will improve the obstacle negotiation ability of the high degree-of-freedoms robot, resulting in a shorter path through adjusting wheelbase of support polygon or trunk height instead of using a fixed configuration for wheeled robots. In the planning framework, we firstly proposed a pre-calculated signed distance field (SDF) mapping method based on point cloud data collected from a lidar sensor and a KD -tree-based point cloud fusion approach. Then, a covariant gradient optimization method is presented, which generates smooth, deformable-configuration, as well as collision-free trajectories in confined narrow spaces. Finally, with the user-defined driving velocity and position as motion inputs, obstacle-avoidancing actions including expanding or shrinking foothold polygon and lifting trunk were effectively testified in realistic conditions, demonstrating the practicability of our methodology. We analyzed the success rate of proposed framework in four different terrain scenarios through deforming configuration rather than bypassing obstacles.

Surface Design of Molding Tool for Composite Component Based on Reverse Engineering

2012 ◽  
Vol 503-504 ◽  
pp. 215-218 ◽  
Author(s):  
Da Wei Wu ◽  
Xiao Fei Ding ◽  
Gang Tong
Keyword(s):  
Reverse Engineering ◽  
Point Cloud ◽  
Laser Scanner ◽  
Surface Fitting ◽  
Point Cloud Data ◽  
Composite Component ◽  
Cross Sectional ◽  
Surface Design ◽  
Cloud Data ◽  
Molding Tool

This paper analyzes the structure of molding tool for composite component, and proposes a method of surface design of molding tool based on reverse engineering. By using handy laser scanner, the point cloud data is obtained from the composite component, which is processed in Geomagic Studio. Then the processed data is imported into CATIA for Surface fitting. The surface of molding tool for composite component is rapidly and accurately designed by analyzing 3D error and comparing cross-sectional data.

scholarly journals Efficient Calculation Method for Tree Stem Traits from Large-Scale Point Clouds of Forest Stands

2021 ◽  
Vol 13 (13) ◽  
pp. 2476
Author(s):  
Hiroshi Masuda ◽  
Yuichiro Hiraoka ◽  
Kazuto Saito ◽  
Shinsuke Eto ◽  
Michinari Matsushita ◽  
...  
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Large Scale ◽  
Random Access ◽  
Point Clouds ◽  
Point Cloud Data ◽  
Forest Stands ◽  
Cross Sectional ◽  
Cloud Data ◽  
Wireframe Model

With the use of terrestrial laser scanning (TLS) in forest stands, surveys are now equipped to obtain dense point cloud data. However, the data range, i.e., the number of points, often reaches the billions or even higher, exceeding random access memory (RAM) limits on common computers. Moreover, the processing time often also extends beyond acceptable processing lengths. Thus, in this paper, we present a new method of efficiently extracting stem traits from huge point cloud data obtained by TLS, without subdividing or downsampling the point clouds. In this method, each point cloud is converted into a wireframe model by connecting neighboring points on the same continuous surface, and three-dimensional points on stems are resampled as cross-sectional points of the wireframe model in an out-of-core manner. Since the data size of the section points is much smaller than the original point clouds, stem traits can be calculated from the section points on a common computer. With the study method, 1381 tree stems were calculated from 3.6 billion points in ~20 min on a common computer. To evaluate the accuracy of this method, eight targeted trees were cut down and sliced at 1-m intervals; actual stem traits were then compared to those calculated from point clouds. The experimental results showed that the efficiency and accuracy of the proposed method are sufficient for practical use in various fields, including forest management and forest research.

scholarly journals Analyzing the Stability of Underground Mines Using 3D Point Cloud Data and Discontinuum Numerical Analysis

2019 ◽  
Vol 11 (4) ◽  
pp. 945 ◽  
Author(s):  
Seung-Joong Lee ◽  
Sung-Oong Choi
Keyword(s):  
Numerical Analysis ◽  
Point Cloud ◽  
Target Surface ◽  
Underground Mines ◽  
Point Cloud Data ◽  
Cross Sectional ◽  
Cloud Data ◽  
Safety Pillar ◽  
Center Axis ◽  
The Stability

This study describes a precise numerical analysis process by adopting the real image of mine openings obtained by light detection and ranging (LiDAR), which can produce a point cloud data by measuring the target surface numerically. The analysis target was a section of an underground limestone mine, to which a hybrid room-and-pillar mining method that was developed to improve ore recovery was applied. It is important that the center axis and the volume of the vertical safety pillar in the lower parts match those in the upper parts. The 3D survey of the target section verified that the center axis of the vertical safety pillar in the lower parts had deviated in a north-westerly direction. In particular, the area of the lower part of the vertical safety pillar was approximately 34 m2 lower than the designed cross-sectional area, which was 100 m2. In order to analyze the stability of the vertical safety pillar, a discontinuum numerical analysis and safety factor analysis were conducted using 3D surveying results. The analysis verified that instability was caused by the joints distributed around the vertical safety pillar. In conclusion, investigation of the 3D survey and 3D numerical analysis techniques performed in this study are expected to provide higher reliability than the current techniques used for establishing whether mining plans require new mining methods or safety measures.

Error Inspection for Turbine Vane Based on Reverse Engineering

2011 ◽  
Vol 338 ◽  
pp. 335-338 ◽  
Author(s):  
Gang Tong ◽  
Yu Zhu Li ◽  
Da Wei Wu ◽  
Xiao Guang Han
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
3D Model ◽  
Laser Scanner ◽  
Point Cloud Data ◽  
3D Laser Scanning ◽  
Cross Sectional ◽  
Inspection Method ◽  
Cloud Data ◽  
Turbine Vane

An error inspection method based on 3D laser scanning measurement is proposed for the purpose of achieving field rapid inspection of turbine vane surface. The 3D model of vane is reconstructed by using the data of form drawing in CATIA. By using handy laser scanner, the point cloud data is obtained from the wood pattern of vane, which is processed in Geomagic Qualify. After registration of vane solid model and point cloud data, the vane surface is rapidly inspected by analyzing 3D error and comparing cross-sectional data.

Registration and Fusion of UAV LiDAR System Sequence Images and Laser Point Clouds

2020 ◽  
Author(s):  
Jiayong Yu ◽  
Longchen Ma ◽  
Maoyi Tian, ◽  
Xiushan Lu
Keyword(s):  
Point Cloud ◽  
Image Data ◽  
Point Clouds ◽  
Optical Image ◽  
Point Cloud Data ◽  
Feature Points ◽  
Lidar System ◽  
Registration Accuracy ◽  
Cloud Data ◽  
Intensity Image

The unmanned aerial vehicle (UAV)-mounted mobile LiDAR system (ULS) is widely used for geomatics owing to its efficient data acquisition and convenient operation. However, due to limited carrying capacity of a UAV, sensors integrated in the ULS should be small and lightweight, which results in decrease in the density of the collected scanning points. This affects registration between image data and point cloud data. To address this issue, the authors propose a method for registering and fusing ULS sequence images and laser point clouds, wherein they convert the problem of registering point cloud data and image data into a problem of matching feature points between the two images. First, a point cloud is selected to produce an intensity image. Subsequently, the corresponding feature points of the intensity image and the optical image are matched, and exterior orientation parameters are solved using a collinear equation based on image position and orientation. Finally, the sequence images are fused with the laser point cloud, based on the Global Navigation Satellite System (GNSS) time index of the optical image, to generate a true color point cloud. The experimental results show the higher registration accuracy and fusion speed of the proposed method, thereby demonstrating its accuracy and effectiveness.

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