scholarly journals Direct Georeferencing for the Images in an Airborne LiDAR System by Automatic Boresight Misalignments Calibration

Sensors ◽  
2020 ◽  
Vol 20 (18) ◽  
pp. 5056
Author(s):  
Haichi Ma ◽  
Hongchao Ma ◽  
Ke Liu ◽  
Wenjun Luo ◽  
Liang Zhang
Keyword(s):  
Control Point ◽  
Digital Camera ◽  
Iteration Process ◽  
Airborne Lidar ◽  
Image Point ◽  
Test Field ◽  
Lidar System ◽  
Ground Control Points ◽  
Source Data ◽  
Point Set

Airborne Light Detection and Ranging (LiDAR) system and digital camera are usually integrated on a flight platform to obtain multi-source data. However, the photogrammetric system calibration is often independent of the LiDAR system and performed by the aerial triangulation method, which needs a test field with ground control points. In this paper, we present a method for the direct georeferencing of images collected by a digital camera integrated in an airborne LiDAR system by automatic boresight misalignments calibration with the auxiliary of point cloud. The method firstly uses an image matching to generate a tie point set. Space intersection is then performed to obtain the corresponding object coordinate values of the tie points, while the elevation calculated from the space intersection is replaced by the value from the LiDAR data, resulting in a new object point called Virtual Control Point (VCP). Because boresight misalignments exist, a distance between the tie point and the image point of VCP can be found by collinear equations in that image from which the tie point is selected. An iteration process is performed to minimize the distance with boresight corrections in each epoch, and it stops when the distance is smaller than a predefined threshold or the total number of epochs is reached. Two datasets from real projects were used to validate the proposed method and the experimental results show the effectiveness of the method by being evaluated both quantitatively and visually.

Boresight Calibration of Airborne LiDAR System Without Ground Control Points

2012 ◽  
Vol 9 (1) ◽  
pp. 85-89 ◽  
Author(s):  
Chen Siying ◽  
Ma Hongchao ◽  
Zhang Yinchao ◽  
Zhong Liang ◽  
Xu Jixian ◽  
...  
Keyword(s):  
Airborne Lidar ◽  
Ground Control ◽  
Control Points ◽  
Lidar System ◽  
Ground Control Points

scholarly journals ASSESSING THE RELIABILITY AND THE ACCURACY OF ATTITUDE EXTRACTED FROM VISUAL ODOMETRY FOR LIDAR DATA GEOREFERENCING

2017 ◽  
Vol XLII-2/W6 ◽  
pp. 201-208
Author(s):  
B. Leroux ◽  
J. Cali ◽  
J. Verdun ◽  
L. Morel ◽  
H. He
Keyword(s):  
Test Bench ◽  
Low Cost ◽  
Control Point ◽  
Visual Odometry ◽  
Airborne Lidar ◽  
Measurement Unit ◽  
Navigation Systems ◽  
Control Points ◽  
Ground Control Points ◽  
Orientation Parameters

Airborne LiDAR systems require the use of Direct Georeferencing (DG) in order to compute the coordinates of the surveyed point in the mapping frame. An UAV platform does not derogate to this need, but its payload has to be lighter than this installed onboard so the manufacturer needs to find an alternative to heavy sensors and navigation systems. For the georeferencing of these data, a possible solution could be to replace the Inertial Measurement Unit (IMU) by a camera and record the optical flow. The different frames would then be processed thanks to photogrammetry so as to extract the External Orientation Parameters (EOP) and, therefore, the path of the camera. The major advantages of this method called Visual Odometry (VO) is low cost, no drifts IMU-induced, option for the use of Ground Control Points (GCPs) such as on airborne photogrammetry surveys. In this paper we shall present a test bench designed to assess the reliability and accuracy of the attitude estimated from VO outputs. The test bench consists of a trolley which embeds a GNSS receiver, an IMU sensor and a camera. The LiDAR is replaced by a tacheometer in order to survey the control points already known. We have also developped a methodology applied to this test bench for the calibration of the external parameters and the computation of the surveyed point coordinates. Several tests have revealed a difference about 2–3 centimeters between the control point coordinates measured and those already known.

scholarly journals PRODUCT ACCURACY EFFECT OF OBLIQUE AND VERTICAL NON-METRIC DIGITAL CAMERA UTILIZATION IN UAV-PHOTOGRAMMETRY TO DETERMINE FAULT PLANE

2016 ◽  
Vol III-6 ◽  
pp. 41-48 ◽  
Author(s):  
C. Amrullah ◽  
D. Suwardhi ◽  
I. Meilano
Keyword(s):  
Fault Plane ◽  
Control Point ◽  
Digital Camera ◽  
Aerial Photographs ◽  
Ground Control ◽  
Control Points ◽  
Point Configuration ◽  
Ground Control Points ◽  
Significant Difference ◽  
Precision And Accuracy

This study aims to see the effect of non-metric oblique and vertical camera combination along with the configuration of the ground control points to improve the precision and accuracy in UAV-Photogrammetry project. The field observation method is used for data acquisition with aerial photographs and ground control points. All data are processed by digital photogrammetric process with some scenarios in camera combination and ground control point configuration. The model indicates that the value of precision and accuracy increases with the combination of oblique and vertical camera at all control point configuration. The best products of the UAV-Photogrammetry model are produced in the form of Digital Elevation Model (DEM) compared to the LiDAR DEM. Furthermore, DEM from UAV-Photogrammetry and LiDAR are used to define the fault plane by using cross-section on the model and interpretation to determine the point at the extreme height of terrain changes. The result of the defined fault planes indicate that two models do not show any significant difference.

scholarly journals PRODUCT ACCURACY EFFECT OF OBLIQUE AND VERTICAL NON-METRIC DIGITAL CAMERA UTILIZATION IN UAV-PHOTOGRAMMETRY TO DETERMINE FAULT PLANE

2016 ◽  
Vol III-6 ◽  
pp. 41-48 ◽  
Author(s):  
C. Amrullah ◽  
D. Suwardhi ◽  
I. Meilano
Keyword(s):  
Fault Plane ◽  
Control Point ◽  
Digital Camera ◽  
Aerial Photographs ◽  
Ground Control ◽  
Control Points ◽  
Point Configuration ◽  
Ground Control Points ◽  
Significant Difference ◽  
Precision And Accuracy

This study aims to see the effect of non-metric oblique and vertical camera combination along with the configuration of the ground control points to improve the precision and accuracy in UAV-Photogrammetry project. The field observation method is used for data acquisition with aerial photographs and ground control points. All data are processed by digital photogrammetric process with some scenarios in camera combination and ground control point configuration. The model indicates that the value of precision and accuracy increases with the combination of oblique and vertical camera at all control point configuration. The best products of the UAV-Photogrammetry model are produced in the form of Digital Elevation Model (DEM) compared to the LiDAR DEM. Furthermore, DEM from UAV-Photogrammetry and LiDAR are used to define the fault plane by using cross-section on the model and interpretation to determine the point at the extreme height of terrain changes. The result of the defined fault planes indicate that two models do not show any significant difference.

scholarly journals LIDAR BATHYMETRIC EVALUATION BASED ON SCATTERING CLASSIFICATION ALGORITHM

2020 ◽  
Vol XLII-3/W10 ◽  
pp. 97-103
Author(s):  
J. Gao ◽  
G. Q. Zhou ◽  
H. Y. Wang ◽  
X. Zhou ◽  
Y. X. Mu ◽  
...  
Keyword(s):  
Attenuation Coefficient ◽  
Classification Algorithm ◽  
Airborne Lidar ◽  
Discrimination Factor ◽  
Actual Situation ◽  
Particle Scattering ◽  
Lidar System ◽  
Scattering Intensity ◽  
Diffuse Attenuation Coefficient ◽  
532 Nm

Abstract. The evaluation of the bathymetric capability of traditional airborne lidar system is mostly based on the formula of bathymetric capability by evaluating the diffuse attenuation coefficient (Kd). This method is derived form the assumption that the reflectance of sediment is fixed. In this study ,however,the reflectance of sediment is not fixed. Therefore, this study improves the ability of bathymetric formula, and proposes a particle scattering classification algorithm to obtain the transmissivity value. The algorithm filters the scattering modes of particles by scattering discrimination factor (q), and obtains the transmissivity values by using the scattering intensity formulas. Experiments show that, when the transmissivity is in the range of 0–1 and the average values of Kd(532 nm) are 0.1150 m−1, 0.0894 m−1 and 0.0903 m−1 in January, June and October respectively, accordingly, the bathymetric capabilities are 0–44 m, 0–61.5 m and 0–52.5 m, respectively. Compared with the original bathymetric method, these results show that the maximum bathymetric value has measured by the improved bathymetric capability formula and scattering classification algorithm has decreased under the influence of the change of sediment reflectance, and the result is more consistent with the actual situation and more accurate.

scholarly journals INTEGRATING TERRESTRIAL LIDAR WITH POINT CLOUDS CREATED FROM UNMANNED AERIAL VEHICLE IMAGERY

2015 ◽  
Vol XL-1/W4 ◽  
pp. 97-101 ◽  
Author(s):  
M. Leslar
Keyword(s):  
Point Cloud ◽  
Inertial Navigation System ◽  
Vertical Wall ◽  
Digital Camera ◽  
Point Clouds ◽  
Digital Photogrammetry ◽  
Lidar System ◽  
Terrestrial Lidar ◽  
Stereo Imagery ◽  
Aerial Vehicle

Using unmanned aerial vehicles (UAV) for the purposes of conducting high-accuracy aerial surveying has become a hot topic over the last year. One of the most promising means of conducting such a survey involves integrating a high-resolution non-metric digital camera with the UAV and using the principals of digital photogrammetry to produce high-density colorized point clouds. Through the use of stereo imagery, precise and accurate horizontal positioning information can be produced without the need for integration with any type of inertial navigation system (INS). Of course, some form of ground control is needed to achieve this result. Terrestrial LiDAR, either static or mobile, provides the solution. Points extracted from Terrestrial LiDAR can be used as control in the digital photogrammetry solution required by the UAV. In return, the UAV is an affordable solution for filling in the shadows and occlusions typically experienced by Terrestrial LiDAR. In this paper, the accuracies of points derived from a commercially available UAV solution will be examined and compared to the accuracies achievable by a commercially available LIDAR solution. It was found that the LiDAR system produced a point cloud that was twice as accurate as the point cloud produced by the UAV’s photogrammetric solution. Both solutions gave results within a few centimetres of the control field. In addition the about of planar dispersion on the vertical wall surfaces in the UAV point cloud was found to be multiple times greater than that from the horizontal ground based UAV points or the LiDAR data.

scholarly journals Infrared and visual image point set registration based on sensor parameters and refined CPD algorithm

2012 ◽  
Vol 31 (2) ◽  
pp. 171-176 ◽  
Author(s):  
Peng WANG ◽  
Ying-Hui GAO ◽  
Ping WANG ◽  
Zhi-Guo QU ◽  
Zhen-Kang SHEN
Keyword(s):  
Visual Image ◽  
Image Point ◽  
Point Set Registration ◽  
Point Set

scholarly journals Experimental Investigation on the Performance of DJI Phantom 4 RTK in the PPK Mode for 3D Mapping Open-Pit Mines

2020 ◽  
Vol 1 (2) ◽  
Author(s):  
Le VAN CANH ◽  
Cao XUAN CUONG ◽  
Nguyen QUOC LONG ◽  
Le THI THU HA ◽  
Tran TRUNG ANH ◽  
...  
Keyword(s):  
Coal Mines ◽  
Daily Basis ◽  
Open Pit ◽  
Surface Model ◽  
Test Field ◽  
Open Pit Mines ◽  
Ground Control Points ◽  
Aerial Vehicle ◽  
Topographic Surveys

Open-pit coal mines’ terrain is often complex and quickly and frequently changes. Therefore, topographic surveys of open-pit mines are undertaken on a daily basis. While these tasks are very time-consuming and costly with traditional methods such as total station and GNSS, the unmanned aerial vehicle (UAV) based method can be more efficient. This method is a combination of the “Structure from motion” (SfM) photogrammetry technique and UAV photogrammetry which has been widely used in topographic surveying. With an increasing popularity of RTK-enabled drones, it is becoming even more powerful method. While the important role of ground control points (GCP) in the accuracy of digital surface model (DSM) generated from images acquired by “traditional” UAVs (not RTK-enabled drones) has been proved in many previous studies, it is not clear in the case of RTK-enabled drones, especially for complex terrain in open-pit coal mines. In this study, we experimentally investigated the influence of GCP regarding its numbers and distribution on the accuracy of DSM generation from images acquired by RTK-enabled drones in open-pit coal mines. In addition, the Post Processing Kinematic (PPK) mode was executed over a test field with the same flight altitude. DSM generation was performed with several block control configurations: PPK only, PPK with one GCP, and PPK with two GCPs. Several positions of GCPs were also examined to test the optimal locations for placing GCPs to achieve accurate DSMs. The results show that the horizontal and vertical accuracy given by PPK only were 9.3 and 84.4 cm, respectively. However, when adding at least one GCP, the accuracy was significantly improved in both horizontal and vertical components, with RMSE for XY and Z ranging between 3.8 and 9.8 cm (with one GCP) and between 3.0 and 5.7 cm (with two GCPs), respectively. Also, the GCPs placed in the deep areas of the open-pit mine could ensure the cm-level accuracy.

scholarly journals Airborne Waveform Lidar Simulator Using the Radiative Transfer of a Laser Pulse

2019 ◽  
Vol 9 (12) ◽  
pp. 2452 ◽  
Author(s):  
Minsu Kim
Keyword(s):  
Radiative Transfer ◽  
Laser Pulse ◽  
Accuracy Assessment ◽  
Three Dimensional ◽  
Finite Size ◽  
Airborne Lidar ◽  
Beam Divergence ◽  
System Response ◽  
Lidar System ◽  
Lidar Equation

An airborne lidar simulator creates a lidar point cloud from a simulated lidar system, flight parameters, and the terrain digital elevation model (DEM). At the basic level, the lidar simulator computes the range from a lidar system to the surface of a terrain using the geomatics lidar equation. The simple computation effectively assumes that the beam divergence is zero. If the beam spot is meaningfully large due to the large beam divergence combined with high sensor altitude, then the beam plane with a finite size interacts with a ground target in a realistic and complex manner. The irradiance distribution of a delta-pulse beam plane is defined based on laser pulse radiative transfer. The airborne lidar simulator in this research simulates the interaction between the delta-pulse and a three-dimensional (3D) object and results in a waveform. The waveform will be convoluted using a system response function. The lidar simulator also computes the total propagated uncertainty (TPU). All sources of the uncertainties associated with the position of the lidar point and the detailed geomatics equations to compute TPU are described. The boresighting error analysis and the 3D accuracy assessment are provided as examples of the application using the simulator.

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