UAV-LiCAM SYSTEM DEVELOPMENT: CALIBRATION AND GEO-REFERENCING

Cortés, Camilo; Shahbazi, Mozhdeh; Ménard, Patrick

doi:10.5194/isprs-archives-xlii-1-107-2018

Cited by 5 publications

(3 citation statements)

References 20 publications

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“…Some studies have proposed multimodal sensor calibration techniques involving two or more LiDAR, or a combination of camera and LiDAR sensors to assist with the calibration. Cortes et al [39] performed a combined bundle adjustment for their UAV MMS to obtain camera-LiDAR mounting parameters. They emphasized the need to consider georeferencing uncertainty in the calibration.…”

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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“…The combination of these two sensors achieves complementary advantages. 7 In order to combine LiDAR sensor and monocular camera, calibration is the first issue needed to be solved. Calibration is mainly the problem of data point matching between the camera coordinate system and the laser coordinate system.…”

Section: Introductionmentioning

confidence: 99%

3D map building based on extrinsic sensor calibration method and object contour detector with a fully convolutional neural network

Dai

Lee

2022

Measurement and Control

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In this paper, we propose a simple method to obtain an object’s 3D coordinate information in one image, using one monocular camera and two 2D LiDAR sensors, which are widely used low-cost sensors. An extrinsic calibration method is used for each LiDAR sensor to transfer LiDAR coordinate to camera pixel coordinate in one image. The proportion factor [Formula: see text] is calculated, based on the relationship of pixel from calibration points and distance from detected points of two LiDAR sensors. In order to create a correct 3D map, a deep learning algorithm for real-time object contour detection with a fully Convolutional Neural Network is proposed, through gathering data, labeling data, training model, and testing object contour detector. For 3D map building, by the proportion factor [Formula: see text] and the detector contour feature pixels, the height of the object is obtained. Combining with LiDAR sensor’s detection information, the 3D posture of the object is obtained. Based on the proposed extrinsic sensor calibration method and the object contour detect result, virtual points of the object are calculated. Finally, the experiment results show the 3D coordinate information of the detected single or multiple objects. Assembling sensors and a controller on a Kobuki robot, the indoor 3D map is built based on the proposed methods.

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“…However, LiDAR only system is not capable of georeferencing and mapping the terrain due to the lack of geographical and positioning information. Therefore, engineers use LiDAR, unmanned aerial vehicle (UAV), Global Positioning System (GPS), and inertial measurement unit (IMU) as the primary devices and sensors to obtain the 3D image of the environment [7,8]. The row LiDAR data are direct-georeferenced by the fusion of GPS and IMU sensors.…”

mentioning

confidence: 99%

Classified 3D mapping and deep learning-aided signal power estimation architecture for the deployment of wireless communication systems

Egi

Eyceyurt

2022

J Wireless Com Network

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The traditional wireless communication systems deployment models require expensive and time-consuming procedures, including environment selection (rural, urban, and suburban), drive test data collection, and analysis of the raw data. These procedures mainly utilize stochastic and deterministic approaches for signal strength prediction to locate the optimum cellular tower (eNodeB) position for 4G and 5G systems. Since environment selection is limited by urban, suburban, and rural areas, they do not cover complex macro and micro variations, especially buildings and tree canopies having a higher impact on signal fading due to scattering and absorption. Therefore, they usually end up with high prediction errors. This article proposes an efficient architecture for the deployment of communication systems. The proposed method determines the effect of the environment via extracting tree and building properties by using a classified 3D map and You Only Look Once (YOLO) V5, which is one of the most efficient deep learning algorithms. According to the results, the mean average precision (mAP) 0.5% and mAP 0.95% accuracies are obtained as 0.96 and 0.45, and image color classification (ICC) findings indicate 77.6% accuracy on vegetation detection, especially for tree canopies. Thus, the obtained results significantly improved signal strength prediction with a 3.96% Mean Absolute Percentage Error (MAPE) rate, while other empirical models’ prediction errors fall in the range of 6.07–15.26%.

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UAV-LiCAM SYSTEM DEVELOPMENT: CALIBRATION AND GEO-REFERENCING

Cited by 5 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

3D map building based on extrinsic sensor calibration method and object contour detector with a fully convolutional neural network

Classified 3D mapping and deep learning-aided signal power estimation architecture for the deployment of wireless communication systems

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