3D Data Fusion Using Unmanned Aerial Vehicle (UAV) Photogrammetry and Terrestrial Laser Scanner (TLS)

The rapid development of the construction world in Indonesia has led to an increase in supporting technology that is more effective and efficient. The Building Information Model (BIM) technology that begins with the creation of an as-built 3D model, this model describes the existing condition of the building. The Terrestrial Laser Scanner (TLS) method can provide a point cloud with a decent point density, but there are still areas of the building that aren't covered, such as the roof. To be more complete and detailed, additional data is needed using an Unmanned Aerial Vehicle (UAV). The results of the combination of TLS and UAV complement each other so that the results of the point cloud can form more detailed buildings. BIM may be built by combining these two data sets, allowing for the three-dimensional depiction of assets in buildings. The registration results for TLS point cloud data have a fairly good value where the overlap value is 44.9% (minimum 30%), balance is 41.2% (minimum 20%), points < 6mm is 98.9% (minimum 90%). The measurement results using the UAV have an RMSE GCP value of 0.266m and an RMSE ICP of 0.455m. Merging the results of TLS and UAV measurements is done using 3DReshaper software with four align points. The final result of making the BIM model is obtained level of detail (LOD) 3 where room models such as columns, floors, stairs, and walls are well depicted, while asset models such as furniture are also depicted although they are still simple objects.

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Use of UAV-Photogrammetry for Quasi-Vertical Wall Surveying

Remote Sensing ◽

10.3390/rs12142221 ◽

2020 ◽

Vol 12 (14) ◽

pp. 2221 ◽

Cited By ~ 2

Author(s):

Patricio Martínez-Carricondo ◽

Francisco Agüera-Vega ◽

Fernando Carvajal-Ramírez

Keyword(s):

Unmanned Aerial Vehicle ◽

Vertical Wall ◽

Laser Scanner ◽

Point Clouds ◽

Ground Control ◽

Terrestrial Laser Scanner ◽

Control Points ◽

Ground Control Points ◽

Aerial Vehicle

In this study, an analysis of the capabilities of unmanned aerial vehicle (UAV) photogrammetry to obtain point clouds from areas with a near-vertical inclination was carried out. For this purpose, 18 different combinations were proposed, varying the number of ground control points (GCPs), the adequacy (or not) of the distribution of GCPs, and the orientation of the photographs (nadir and oblique). The results have shown that under certain conditions, the accuracy achieved was similar to those obtained by a terrestrial laser scanner (TLS). For this reason, it is necessary to increase the number of GCPs as much as possible in order to cover a whole study area. In the event that this is not possible, the inclusion of oblique photography ostensibly improves results; therefore, it is always advisable since they also improve the geometric descriptions of break lines or sudden changes in slope. In this sense, UAVs seem to be a more economic substitute compared to TLS for vertical wall surveying.

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Case study of terrain measurement using terrestrial laser scanner and Unmanned Aerial Vehicle mounted laser scanner

Journal of the Japan society of photogrammetry and remote sensing ◽

10.4287/jsprs.57.70 ◽

2018 ◽

Vol 57 (2) ◽

pp. 70-73

Author(s):

Michihito TAKATO

Keyword(s):

Unmanned Aerial Vehicle ◽

Laser Scanner ◽

Terrestrial Laser Scanner ◽

Aerial Vehicle

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Road Maintenance Management System Using 3D Data by Terrestrial Laser Scanner and UAV

Proceedings of the 37th International Symposium on Automation and Robotics in Construction (ISARC) ◽

10.22260/isarc2020/0184 ◽

2020 ◽

Author(s):

Satoshi Kubota ◽

Ryosuke Hata ◽

Kotaro Nishi ◽

Chiyuan Ho ◽

Yoshihiro Yasumuro

Keyword(s):

Management System ◽

Laser Scanner ◽

Maintenance Management ◽

Terrestrial Laser Scanner ◽

Road Maintenance ◽

3D Data

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Wheat Leaf Area Index Prediction Using Data Fusion Based on High-Resolution Unmanned Aerial Vehicle Imagery

10.21203/rs.3.rs-1169494/v1 ◽

2021 ◽

Author(s):

Shuang Wu ◽

Lei Deng ◽

Lijie Guo ◽

Yanjie Wu

Keyword(s):

High Resolution ◽

Data Fusion ◽

Leaf Area Index ◽

Leaf Area ◽

Unmanned Aerial Vehicle ◽

Prediction Accuracy ◽

Sensor Data ◽

Support Vector ◽

Area Index ◽

Aerial Vehicle

Abstract Background: Leaf Area Index (LAI) is half of the amount of leaf area per unit horizontal ground surface area. Consequently, accurate vegetation extraction in remote sensing imagery is critical for LAI estimation. However, most studies do not fully exploit the advantages of Unmanned Aerial Vehicle (UAV) imagery with high spatial resolution, such as not removing the background (soil and shadow, etc.). Furthermore, the advancement of multi-sensor synchronous observation and integration technology allows for the simultaneous collection of canopy spectral, structural, and thermal data, making it possible for data fusion.Methods: To investigate the potential of high-resolution UAV imagery combined with multi-sensor data fusion in LAI estimation. High-resolution UAV imagery was obtained with a multi-sensor integrated MicaSense Altum camera to extract the wheat canopy's spectral, structural, and thermal features. After removing the soil background, all features were fused, and LAI was estimated using Random Forest and Support Vector Machine Regression.Result: The results show that: (1) the soil background reduced the accuracy of the LAI prediction, and soil background could be effectively removed by taking advantage of high-resolution UAV imagery. After removing the soil background, the LAI prediction accuracy improved significantly, R2 raised by about 0.27, and RMSE fell by about 0.476. (2) The fusion of multi-sensor synchronous observation data improved LAI prediction accuracy and achieved the best accuracy (R2 = 0.815 and RMSE = 1.023). (3) When compared to other variables, 23 CHM, NRCT, NDRE, and BLUE are crucial for LAI estimation. Even the simple Multiple Linear Regression model could achieve high prediction accuracy (R2 = 0.679 and RMSE = 1.231), providing inspiration for rapid and efficient LAI prediction.Conclusions: The method of this study can be transferred to other sites with more extensive areas or similar agriculture structures, which will facilitate agricultural production and management.

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Data fusion of terrestrial laser scanner and remotely piloted aircraft systems points clouds for monitoring the coastal protection systems

Aquatic Ecosystem Health & Management ◽

10.1080/14634988.2020.1807299 ◽

2020 ◽

pp. 1-7

Author(s):

M. Saponaro ◽

L. Pratola ◽

A. Capolupo ◽

A. Saponieri ◽

L. Damiani ◽

...

Keyword(s):

Data Fusion ◽

Laser Scanner ◽

Coastal Protection ◽

Terrestrial Laser Scanner ◽

Aircraft Systems ◽

Protection Systems ◽

Remotely Piloted Aircraft

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Numerical Simulation and Experimental Verification of the Deposition Concentration of an Unmanned Aerial Vehicle

Applied Engineering in Agriculture ◽

10.13031/aea.13221 ◽

2019 ◽

Vol 35 (3) ◽

pp. 367-376 ◽

Cited By ~ 1

Author(s):

Qiang Shi ◽

Hanping Mao ◽

Xianping Guan

Keyword(s):

Numerical Simulation ◽

Flow Field ◽

Unmanned Aerial Vehicle ◽

Simulation Analysis ◽

Three Dimensional ◽

Laser Scanner ◽

Utilization Rate ◽

Droplet Deposition ◽

Aerial Vehicle ◽

Motion Characteristics

Abstract. To analyze the droplet deposition under the influence of the flow field of an unmanned aerial vehicle (UAV), a hand-held three-dimensional (3D) laser scanner was used to scan 3D images of the UAV. Fluent software was used to simulate the motion characteristics of droplets and flow fields under the conditions of a flight speed of 3 m/s and an altitude of 1.5 m. The results indicated that the ground deposition concentration in the nonrotor flow field was high, the spray field width was 2.6 m, and the droplet deposition concentration was 50 to 200 ug/cm2. Under the influence of the rotor flow field, the widest deposition range of droplets reached 12.8 m. Notably, the droplet deposition uniformity worsened, and the concentration range of the droplet deposition was 0 to 500 ug/cm2. With the downward development of the downwash flow field, the overall velocity of the flow field gradually decreased, and the influence interval of the flow field gradually expanded. In this article, the droplet concentration was verified under simulated working conditions by a field experiment, thereby demonstrating the reliability of the numerical simulation results. This research could provide a basis for determining optimal UAV operating parameters, reducing the drift of droplets and increasing the utilization rate of pesticides. Keywords: Unmanned aerial vehicle (UAV), Aerial application, Downwash flow field, Droplet deposition, Simulation analysis.

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Real-Time Onboard 3D State Estimation of an Unmanned Aerial Vehicle in Multi-Environments Using Multi-Sensor Data Fusion

Sensors ◽

10.3390/s20030919 ◽

2020 ◽

Vol 20 (3) ◽

pp. 919 ◽

Cited By ~ 3

Author(s):

Hao Du ◽

Wei Wang ◽

Chaowen Xu ◽

Ran Xiao ◽

Changyin Sun

Keyword(s):

Data Fusion ◽

State Estimation ◽

Real Time ◽

Unmanned Aerial Vehicle ◽

Position Estimation ◽

Sensor Data ◽

Measurement Unit ◽

Sensor Data Fusion ◽

Multi Sensor Data Fusion ◽

Aerial Vehicle

The question of how to estimate the state of an unmanned aerial vehicle (UAV) in real time in multi-environments remains a challenge. Although the global navigation satellite system (GNSS) has been widely applied, drones cannot perform position estimation when a GNSS signal is not available or the GNSS is disturbed. In this paper, the problem of state estimation in multi-environments is solved by employing an Extended Kalman Filter (EKF) algorithm to fuse the data from multiple heterogeneous sensors (MHS), including an inertial measurement unit (IMU), a magnetometer, a barometer, a GNSS receiver, an optical flow sensor (OFS), Light Detection and Ranging (LiDAR), and an RGB-D camera. Finally, the robustness and effectiveness of the multi-sensor data fusion system based on the EKF algorithm are verified by field flights in unstructured, indoor, outdoor, and indoor and outdoor transition scenarios.

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