- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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NRLI-UAV: Non-rigid registration of sequential raw laser scans and images for low-cost UAV LiDAR point cloud quality improvement
摘要： Accurate registration of light detection and ranging (LiDAR) point clouds and images is a prerequisite for integrating the spectral and geometrical information collected by low-cost unmanned aerial vehicle (UAV) systems. Most registration approaches take the directly georeferenced LiDAR point cloud as a rigid body, based on the assumption that the high-precision positioning and orientation system (POS) in the LiDAR system provides sufficient precision, and that the POS errors are negligible. However, due to the large errors of the low-precision POSs commonly used in the low-cost UAV LiDAR systems (ULSs), dramatic deformation may exist in the directly georeferenced ULS point cloud, resulting in non-rigid transformation between the images and the deformed ULS point cloud. As a result, registration may fail when using a rigid transformation between the images and the directly georeferenced LiDAR point clouds. To address this problem, we proposed NRLI-UAV, which is a non-rigid registration method for registration of sequential raw laser scans and images collected by low-cost UAV systems. NRLI-UAV is a two-step registration method that exploits trajectory correction and discrepancy minimization between the depths derived from structure from motion (SfM) and the raw laser scans to achieve LiDAR point cloud quality improvement. Firstly, the coarse registration procedure utilizes global navigation satellite system (GNSS) and inertial measurement unit (IMU)-aided SfM to obtain accurate image orientation and corrects the errors of the low-precision POS. Secondly, the fine registration procedure transforms the original 2D-3D registration to 3D-3D registration. This is performed by setting the oriented images as the reference, and iteratively minimizing the discrepancy between the depth maps derived from SfM and the raw laser scans, resulting in accurate registration between the images and the LiDAR point clouds. In addition, an improved LiDAR point cloud is generated in the mapping frame. Experiments were conducted with data collected by a low-cost UAV system in three challenging scenes to evaluate NRLI-UAV. The final registration errors of the images and the LiDAR point cloud are less than one pixel in image space and less than 0.13 m in object space. The LiDAR point cloud quality was also evaluated by plane fitting, and the results show that the LiDAR point cloud quality is improved by 8.8 times from 0.45 m (root-mean-square error [RMSE] of plane fitting) to 0.05 m (RMSE of plane fitting) using NRLI-UAV, demonstrating a high level of automation, robustness, and accuracy.
关键词： Low-cost,Light detection and ranging (LiDAR),Unmanned aerial vehicle (UAV),Image sequence,Non-rigid registration
更新于2025-09-11 14:15:04
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Fast Phenomics in Vineyards: Development of GRover, the Grapevine Rover, and LiDAR for Assessing Grapevine Traits in the Field
摘要： This paper introduces GRover (the grapevine rover), an adaptable mobile platform for the deployment and testing of proximal imaging sensors in vineyards for the non-destructive assessment of trunk and cordon volume and pruning weight. A SICK LMS-400 light detection and ranging (LiDAR) radar mounted on GRover was capable of producing precise (±3 mm) 3D point clouds of vine rows. Vineyard scans of the grapevine variety Shiraz grown under different management systems at two separate locations have demonstrated that GRover is able to successfully reproduce a variety of vine structures. Correlations of pruning weight and vine wood (trunk and cordon) volume with LiDAR scans have resulted in high coefficients of determination (R2 = 0.91 for pruning weight; 0.76 for wood volume). This is the first time that a LiDAR of this type has been extensively tested in vineyards. Its high scanning rate, eye safe laser and ability to distinguish tissue types make it an appealing option for further development to offer breeders, and potentially growers, quantified measurements of traits that otherwise would be difficult to determine.
关键词： phenomics,proximal sensing,grapevine,light detection and ranging (LiDAR)
更新于2025-09-10 09:29:36
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia, Spain (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - An Approach to Tree Species Classification Using Voxel Neighborhood Density-Based Subsampling of Multiscan Terrestrial Lidar Data
摘要： The knowledge on the species of individual trees is ineluctable for accurate forest parameter estimation and related studies. Terrestrial Laser Scanning (TLS) remote sensing systems acquire a huge number of point samples that contain very accurate and detailed three dimensional (3D) information of tree structures. Every tree species has unique internal and external crown structural characteristics that can be modeled from its TLS data. However, methods in the state of the art show reduced performance due to inaccurate modeling of tree structures such as the crown, and the branch, and poor selection of features. The proposed method leverages on the fine internal and external crown structural information in TLS data to achieve species classification. We remove noise and stem points in TLS data using a novel voxel neighborhood density-based technique. Internal and external crown geometric features derived from the branch level, and the crown level, respectively, are provided to a nonlinear Support Vector Machines (SVM) to achieve species classification, and evaluate feature relevance. All experiments were conducted on a set of 75 manually delineated trees belonging to the Spruce, the Pine, and the Birch species.
关键词： Tree Species,Remote Sensing,Classification,Terrestrial Laser Scanning (TLS),Light Detection and Ranging (LiDAR)
更新于2025-09-10 09:29:36
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Sistema sensor com camera USB para uso em experimentos de polariza??o da luz
摘要： This work shows a sensor system for educational experiments, composed of a USB camera and a software developed and provided by the authors. The sensor system is suitable for the purpose of studying phenomena related to the polarization of the light. The system was tested in experiments performed to verify the Malus’ Law and the spectral efficiency of polarizers. Details of the experimental setup are shown. The camera captures the light in the visible spectral range from a LED that illuminates a white screen after passing through two polarizers. The software uses the image captured by the camera to provide the relative intensity of the light. With the use of two rotating H-sheet linear polarizers, a linear fitting of the Malus’s Law to the transmitted light intensity data resulted in correlation coefficients R larger than 0.9988. The efficiency of the polarizers in different visible spectral regions was verified with the aid of color filters added to the experimental setup. The system was also used to evaluate the intensity time stability of a white LED.
关键词： Light detection,Malus’ Law,Polarization
更新于2025-09-09 09:28:46
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A Liquid Crystal Tunable Filter-Based Hyperspectral LiDAR System and Its Application on Vegetation Red Edge Detection
摘要： In this letter, a hyperspectral light detection and ranging (HSL) with 10-nm spectral resolution was designed and tested using a supercontinuum laser source. The major difference between the prototyped HSL and similar instruments was that a liquid crystal tunable ?lter (LCTF) was installed before the avalanche photodiode detector and utilized as a spectroscopic device. The design allowed continuous wavelength selection of the backscattered echoes in the time dimension. Moreover, for general accuracy evaluation of range measurement and spectral measurement, laboratory experiments for vegetation red edge detection were performed using the prototyped HSL to assess its feasibility on agriculture application. Yellow and green leaves from aloe and dracaena plants were measured by the LCTF-HSL for detecting the corresponding “red edge” position. Spectral pro?les measured by an SVC-HR-1024 spectrometer which is designed by SVC company were used as a reference to evaluate the measurements of HSL. The comparison results showed that the red edge positions extracted from the two individual measurements were similar, thus indicating that the LCTF-based high-resolution HSL was effective for this application.
关键词： red edge,liquid crystal tunable ?lter (LCTF),Hyperspectral light detection and ranging (LiDAR) (HSL)
更新于2025-09-09 09:28:46
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Pixels and 3-D Points Alignment Method for the Fusion of Camera and LiDAR Data
摘要： The fusion of light detection and ranging (LiDAR) and camera data is a promising approach to improve the environmental perception and recognition for intelligent vehicles because of the combination of depth and color information. One of the dif?culties in achieving the fusion is the accurate alignment of the 3-D points with the image pixels. Current methods of data alignment involve the steps of estimating the camera intrinsic parameters and developing a transformation matrix between the camera and LiDAR frame. The drawback of these methods is the accumulation of errors during the calculation of the camera intrinsic parameters and the transformation matrix. In order to improve the data alignment accuracy, we propose a novel algorithm that directly calculates the alignment between the 3-D points and the pixels without the need for camera parameters and calibration of the coordinate transformation matrix. We call the proposed method the pixel and 3-D point alignment (PPA) method. The alignment procedure is achieved by using the extracted corresponding points. First, we calculate a linear alignment matrix without considering the image distortion; and second, we optimize the parameters using the maximum likelihood estimation to consider the camera distortion. Simulation and experimental results indicate that the PPA method is able to align the 3-D points in LiDAR frame with the pixels in image frame with higher accuracy and increased robustness against noise in calibration process than comparable state-of-the-art methods.
关键词： intelligent vehicle,sensor fusion.,calibration,camera and light detection and ranging (LiDAR),Autonomous driving
更新于2025-09-04 15:30:14
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SPAD-Based LiDAR Sensor in 0.35 μm Automotive CMOS with Variable Background Light Rejection
摘要： We present a SPAD-based LiDAR sensor fabricated in an automotive certified 0.35 μm CMOS process. Since reliable sensor operation in high ambient light environment is a crucial factor in automotive applications, four SPADs are implemented in each pixel to suppress ambient light by the detection of photon coincidences. By pixel individual adjustment of the coincidence parameters to the present ambient light condition, an almost constant measurement performance is achieved for a wide range of different target reflectance and ambient illumination levels. This technique allows the acquisition of high dynamic range scenes in a single laser shot. For measurement and demonstration purpose a LiDAR camera with the developed sensor has been built.
关键词： time-of-flight (TOF),background rejection,single-photon avalanche diode (SPAD),range imaging,light detection and ranging (LiDAR),photon coincidence
更新于2025-09-04 15:30:14