Automatic Calibration Method for Airborne LiDAR Systems Based on Approximate Corresponding Points Model

Tian, Yu; Zhao, Yibo; Lei, Shaogang; Ji, Chuning; Duan, Lei; Sedlák, Vladimír

doi:10.1155/2022/4853419

Cited by 3 publications

(4 citation statements)

References 20 publications

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“…Through experiments, the authors reported an absolute accuracy of 3 -5 cm for image and LiDAR point clouds. More recently, Tian et al [43] determined lever arm and boresight parameters separately in two steps. The lever arm parameters were estimated with the help of ground control points (GCP) and SFM modeling using camera images.…”

Section: In-situ Multimodal Sensor Calibration Techniques Involving T...mentioning

confidence: 99%

In-Situ Calibration and Trajectory Enhancement of UAV LiDAR Systems for Mapping Mechanized Agricultural Fields

Manish,

Habib

2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Uncrewed aerial vehicles (UAV) carrying sensors such as light detection and ranging (LiDAR) and multiband cameras georeferenced by an onboard global navigation satellite system/inertial navigation system (GNSS/INS) have become a popular means to quickly acquire near-proximal agricultural remote sensing data. These platforms have bridged the gap between high-altitude airborne and ground-based measurements. UAV data acquisitions also allow for surveying remote sites that are logistically difficult to access from ground. With that said, deriving well-georeferenced mapping products from these mobile mapping systems (MMS) is contingent on accurate determination of platform trajectory along with inter-sensor positional and rotational relationships, that is, the mounting parameters of various sensors with respect to the GNSS/INS unit. Conventional techniques for estimating LiDAR mounting parameters (also referred to as LiDAR system calibration) require carefully planned trajectory and target configuration. Such techniques are time-consuming, and in certain cases, not feasible to accomplish.In this work, an in-situ system calibration and trajectory enhancement strategy for UAV LiDAR is proposed. The strategy uses planting geometry in mechanized agricultural fields through an automated procedure for feature extraction/matching and using them to enhance the quality of LiDAR-derived point clouds. The proposed approach is qualitatively and quantitively evaluated using calibration datasets as well as separately acquired validation datasets to demonstrate the performance of the developed procedure. Quantitatively, the accuracy of the resulting UAV point clouds after system calibration and an accompanying trajectory enhancement improved from as much as 43 cm to 4 cm.

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Section: In-situ Multimodal Sensor Calibration Techniques Involving T...mentioning

confidence: 99%

In-Situ Calibration and Trajectory Enhancement of UAV LiDAR Systems for Mapping Mechanized Agricultural Fields

Manish,

Habib

2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…Other methods introduce artificial control points [12] and reflective targets [13] in the scene. Others rely on matching points [14], profiles [15] and features [16] in two overlapping LiDAR scans. Variants of the iterative closest point (ICP) [17] heuristic have also been used in several studies [14,18].…”

Section: Ins Rotationmentioning

confidence: 99%

“…Others rely on matching points [14], profiles [15] and features [16] in two overlapping LiDAR scans. Variants of the iterative closest point (ICP) [17] heuristic have also been used in several studies [14,18]. It is worth noting that the boresight alignment problem bears a resemblance to the classic point set registration problem [19,20].…”

Section: Ins Rotationmentioning

confidence: 99%

Globally Optimal Boresight Alignment of UAV-LiDAR Systems

Gopinath¹,

Hijazi²,

Collins³

et al. 2022

Preprint

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In airborne light detection and ranging (LiDAR) systems, misalignments between the LiDAR-scanner and the inertial navigation system (INS) mounted on an unmanned aerial vehicle (UAV)'s frame can lead to inaccurate 3D point clouds. Determining the orientation-offset, or boresight error, is key to many LiDAR-based applications. In this work, we introduce a mixed-integer quadratically constrained quadratic program (MIQCQP) that can globally solve this misalignment problem. We also propose a nested spatial branch and bound (nsBB) algorithm that improves computational performance. The nsBB relies on novel preprocessing steps that progressively reduce the problem size. In addition, an adaptive grid search (aGS) allowing us to obtain quick heuristic solutions is presented. Our algorithms are open-source, multi-threaded and multimachine compatible.

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“…Physical models model the errors of LiDAR based on physical parameters, such as ranging, angle measurement, and quadrature errors, while empirical models are based on empirical parameters [ 14 , 15 , 16 , 17 ]. To reduce the correlation among model parameters, these parameters can be treated as observations with prior information [ 18 , 19 , 20 ], or the strongly correlated parameters are grouped and calibrated step by step [ 21 , 22 ].…”

Section: Introductionmentioning

confidence: 99%

Fast Positioning Model and Systematic Error Calibration of Chang’E-3 Obstacle Avoidance Lidar for Soft Landing

Wang¹,

Chen

et al. 2022

Sensors

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Chang‘E-3 is China’s first soft landing mission on an extraterrestrial celestial body. The laser Three-Dimensional Imaging (TDI) sensor is one of the key payloads of the Chang‘E-3 lander. Its main task is to provide accurate 3D lunar surface information of the target landing area in real time for the selection of safe landing sites. Here, a simplified positioning model was constructed, to meet the accuracy and processing timeline requirements of the TDI sensor of Chang‘E-3. By analyzing the influence of TDI intrinsic parameters, a permanent outdoor calibration field based on flat plates was specially designed and constructed, and a robust solution of the geometric calibration adjustment was realized by introducing virtual observation equations for unknowns. The geometric calibration and its absolute and relative positioning accuracy verification were carried out using multi-measurement and multi-angle imaging data. The results show that the error of TDI intrinsic parameters will produce a false obstacle with a maximum height of about 1.4 m on the plane, which will cause the obstacle avoidance system of Chang‘E-3 to fail to find a suitable landing area or find a false flat area. Furthermore, the intrinsic parameters of the TDI have good stability and the accuracy of the reconstructed three-dimensional surface can reach about 4 cm after error calibration, which provides a reliable terrain guarantee for the autonomous obstacle avoidance of the Chang‘E-3 lander.

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Automatic Calibration Method for Airborne LiDAR Systems Based on Approximate Corresponding Points Model

Cited by 3 publications

References 20 publications

In-Situ Calibration and Trajectory Enhancement of UAV LiDAR Systems for Mapping Mechanized Agricultural Fields

In-Situ Calibration and Trajectory Enhancement of UAV LiDAR Systems for Mapping Mechanized Agricultural Fields

Globally Optimal Boresight Alignment of UAV-LiDAR Systems

Fast Positioning Model and Systematic Error Calibration of Chang’E-3 Obstacle Avoidance Lidar for Soft Landing

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