- 标题
- 摘要
- 关键词
- 实验方案
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[IEEE 2018 26th International Conference on Geoinformatics - Kunming (2018.6.28-2018.6.30)] 2018 26th International Conference on Geoinformatics - Multilevel Solar Potential Analysis of Building Based on Ubiquitous Point Clouds
摘要： Solar potential analysis is essential for Building Information Modeling (BIM) applications, like photovoltaic installation. The estimation of solar potential on rooftops has been widely discussed, whereas the study on fa?ade is still limited. Benefit from the development of various sensors, ubiquitous point clouds are now widely and easily captured by photogrammetry, laser scanning or other technologies, to represent the building geometry. This paper proposes a method for solar potential analysis on both rooftops and fa?ades, using ubiquitous point cloud collected by Unmanned Aerial Vehicle (UAV) and Terrestrial Laser Scanning (TLS). One building with different orientations is selected for the case study. Results show that the proposed method is valid for multilevel solar potential analysis of buildings.
关键词： laser scanning,solar potential,ubiquitous point cloud,Building Information Modeling (BIM)
更新于2025-09-23 15:23:52
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[IEEE 2018 25th International Conference on Mechatronics and Machine Vision in Practice (M2VIP) - Stuttgart, Germany (2018.11.20-2018.11.22)] 2018 25th International Conference on Mechatronics and Machine Vision in Practice (M2VIP) - 3D Point Cloud Coarse Registration based on Convex Hull Refined by ICP and NDT
摘要： Non-rigid registration is a crucial step for many applications such as motion tracking, model retrieval, and object recognition. The accuracy of these applications is highly dependent on the initial position used in registration step. In this paper we propose a novel Convex Hull Aided Coarse Registration refined by two algorithms applied on projected points.Firstly,the proposed approach uses a statistical method to find the best plane that represents each point cloud. Secondly, all the points of each cloud are projected onto the corresponding planes. Then, two convex hulls are extracted from the two projected point sets and then matched optimally. Next, the non-rigid transformation from the reference to the model is robustly estimated through minimizing the distance between the matched point's pairs of the two convex hulls.Finally, this transformation estimation is refined by two methods. The first one is the refinement of coarse registration by Iterative Closest Point (ICP). The second one consists of the refinement of coarse registration by the Normal Distribution Transform (NDT). An experimental study ,carried out on several clouds, shows that the refinement of coarse registration with ICP gives, in the most cases, a better result than refinement with NDT.
关键词： Iterative Closest Point (ICP),Convex Hull,Normal Distribution Transform (NDT),Non rigid registration,3D point cloud,Principal Component Analysis (PCA)
更新于2025-09-23 15:22:29
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[IEEE 2018 IEEE International Conference on Intelligent Transportation Systems (ITSC) - Maui, HI, USA (2018.11.4-2018.11.7)] 2018 21st International Conference on Intelligent Transportation Systems (ITSC) - Vehicle Detection and Localization using 3D LIDAR Point Cloud and Image Semantic Segmentation
摘要： This paper presents a real-time approach to detect and localize surrounding vehicles in urban driving scenes. We propose a multimodal fusion framework that processes both 3D LIDAR point cloud and RGB image to obtain robust vehicle position and size in a Bird's Eye View (BEV). Semantic segmentation from RGB images is obtained using our efficient Convolutional Neural Network (CNN) architecture called ERFNet. Our proposal takes advantage of accurate depth information provided by LIDAR and detailed semantic information processed from a camera. The method has been tested using the KITTI object detection benchmark. Experiments show that our approach outperforms or is on par with other state-of-the-art proposals but our CNN was trained in another dataset, showing a good generalization capability to any domain, a key point for autonomous driving.
关键词： localization,ERFNet,image semantic segmentation,KITTI,autonomous driving,vehicle detection,CNN,point cloud,multimodal fusion,3D LIDAR
更新于2025-09-23 15:22:29
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A greyscale voxel model for airborne lidar data applied to building detection
摘要： The existing binary voxel model algorithm for 3D building detection (3BD) from airborne lidar cannot distinguish between connected buildings and non-buildings. As a result, a greyscale voxel structure model, using the discretised mean intensity of lidar points, is presented to support subsequent building detection in areas where buildings are adjacent to non-buildings but with different greyscales. The resulting 3BD algorithm first detects a building roof by selecting voxels characterised by a jump in elevation as seeds, labelling them and their 3D connected regions as rooftop voxels. Then voxels which fall into buffers and possess similar greyscales to that of the corresponding building outline are assigned as building facades. The results for detected buildings are evaluated using lidar data with different densities and demonstrate a high rate of success.
关键词： lidar,greyscale,voxel,building detection,point cloud,intensity
更新于2025-09-23 15:22:29
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Dection and Health Analysis of Individual Tree in Urban Environment with Multi-Sensor Platform
摘要： With the technology enhanced, 3D mobile light detection and ranging (LiDAR) can produce more accurate 3D information for the objects. Meanwhile, hyperspectral remote sensing has more number of wavelengths and provides a higher resolution spectrum of objects. This paper proposes a multi-sensor platform to provide these two data for health detection at the individual tree level in urban environments. We firstly locate and segment the suspected tree objects by ground removal and Euclidean distance clustering. Then we take use of spectrum to remove non-tree objects, e.g., buildings, light poles. After that, we use LiDAR data to compute the geometric parameters of each tree and hyperspectral data to analyze its health situation.
关键词： point cloud,hyperspectral,spectrum,LiDAR,individual tree detection,health monitoring
更新于2025-09-23 15:22:29
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GPU-accelerated integral imaging and full-parallax 3D display using stereo–plenoptic camera system
摘要： In this paper, we propose a novel approach to produce integral images ready to be displayed onto an integral-imaging monitor. Our main contribution is the use of commercial plenoptic camera, which is arranged in a stereo configuration. Our proposed set-up is able to record the radiance, spatial and angular, information simultaneously in each different stereo position. We illustrate our contribution by composing the point cloud from a pair of captured plenoptic images, and generate an integral image from the properly registered 3D information. We have exploited the graphics processing unit (GPU) acceleration in order to enhance the integral-image computation speed and efficiency. We present our approach with imaging experiments that demonstrate the improved quality of integral image. After the projection of such integral image onto the proposed monitor, 3D scenes are displayed with full-parallax.
关键词： Stereo camera,3D data registration,Point cloud,Plenoptic camera,GPU,3D display,Integral imaging
更新于2025-09-23 15:22:29
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Evaluation of Ground Surface Models Derived from Unmanned Aerial Systems with Digital Aerial Photogrammetry in a Disturbed Conifer Forest
摘要： Detailed vertical forest structure information can be remotely sensed by combining technologies of unmanned aerial systems (UAS) and digital aerial photogrammetry (DAP). A key limitation in the application of DAP methods, however, is the inability to produce accurate digital elevation models (DEM) in areas of dense vegetation. This study investigates the terrain modeling potential of UAS-DAP methods within a temperate conifer forest in British Columbia, Canada. UAS-acquired images were photogrammetrically processed to produce high-resolution DAP point clouds. To evaluate the terrain modeling ability of DAP, first, a sensitivity analysis was conducted to estimate optimal parameters of three ground-point classification algorithms designed for airborne laser scanning (ALS). Algorithms tested include progressive triangulated irregular network (TIN) densification (PTD), hierarchical robust interpolation (HRI) and simple progressive morphological filtering (SMRF). Points were classified as ground from the ALS and served as ground-truth data to which UAS-DAP derived DEMs were compared. The proportion of area with root mean square error (RMSE) <1.5 m were 56.5%, 51.6% and 52.3% for the PTD, HRI and SMRF methods respectively. To assess the influence of terrain slope and canopy cover, error values of DAP-DEMs produced using optimal parameters were compared to stratified classes of canopy cover and slope generated from ALS point clouds. Results indicate that canopy cover was approximately three times more influential on RMSE than terrain slope.
关键词： Structure from Motion (SfM),point cloud classification,unmanned aerial systems (UAS),digital elevation model (DEM)
更新于2025-09-23 15:22:29
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An Improved Top-Hat Filter with Sloped Brim for Extracting Ground Points from Airborne Lidar Point Clouds
摘要： Airborne light detection and ranging (lidar) has become a powerful support for acquiring geospatial data in numerous geospatial applications and analyses. However, the process of extracting ground points accurately and effectively from raw point clouds remains a big challenge. This study presents an improved top-hat filter with a sloped brim to enhance the robustness of ground point extraction for complex objects and terrains. The top-hat transformation is executed and the elevation change intensity of the transitions between the obtained top-hats and outer brims is inspected to suppress the omission error caused by protruding terrain features. Finally, the nonground objects of complex structures, such as multilayer buildings, are identified by the brim filter that is extended outward. The performance of the proposed filter in various environments is evaluated using diverse datasets with difficult cases. The comparison of the proposed filter with the commercial software Terrasolid TerraScan and other popular filtering algorithms demonstrates the applicability and effectiveness of this filter. Experimental results show that the proposed filter has great promise in terms of its application in various types of landscapes. Abrupt terrain features with dramatic elevation changes are well preserved, and diverse objects with complicated shapes are effectively removed. This filter has minimal omission and commission error oscillation for different test areas and thus demonstrates a stable and reliable performance in diverse landscapes. In addition, the proposed algorithm has high computational efficiency because of its simple and efficient data structure and implementation.
关键词： point cloud,top-hat transformation,mathematical morphology,filtering,lidar
更新于2025-09-23 15:22:29
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[IEEE 2018 IEEE International Conference on Automation/XXIII Congress of the Chilean Association of Automatic Control (ICA-ACCA) - Concepcion, Chile (2018.10.17-2018.10.19)] 2018 IEEE International Conference on Automation/XXIII Congress of the Chilean Association of Automatic Control (ICA-ACCA) - Mobile LiDAR Scanner for the Generation of 3D Georeferenced Point Clouds
摘要： Mobile laser scanning systems are a modern tool used by leading companies in surveying. These systems are capable of making a three-dimensional reconstruction of the environment by capturing thousands of aligned points. This article describes a hardware and software-based solution for a 3D LiDAR scanner capable of generating a georeferenced point cloud. This solution uses an integrated microcomputer-based hardware architecture, integration of navigation components and data logging. In addition, the effect caused by the measurement errors of the inertial sensors is displayed. To minimize these undesired effects, the use of high-precision navigation system is necessary. For the estimation of the position and orientation of the data captured by the LiDAR sensor, a non-linear interpolation is used for the oversampling of navigation data. Likewise, the scientific problem of direct georeferencing is modeled with a mathematical approach to conventional robotic structure. The product developed meets the technical requirements for most applications in topographic surveys and structural modeling. The system is portable on multiple platforms such as land vehicles and unmanned aerial vehicles.
关键词： point cloud,LiDAR,georeferencing,INS,MLS,hardware
更新于2025-09-23 15:22:29
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Real-Time Geometric Parameter Measurement of High-Speed Railway Fastener Based on Point Cloud from Structured Light Sensors
摘要： With the increase in the number of service years for high-speed railways, the foundation of the rail track suffers from settlement, which causes rail track irregularity. To adjust the position of the track and meet track regularity demands, several components of the fastening system will be replaced by different sized components. It is important to measure the exact geometric parameters for the components of a fastening system before adjusting the track. Currently, the measurement process is conducted manually, which is laborious and error-prone. In this paper, a real-time geometric parameter measurement system for high-speed railway fastener based on 2-D laser profilers is presented. Dense and precise 3-D point clouds of high-speed railway fasteners are obtained from the system. A fastener extraction method is presented to extract fastener point cloud and a region-growing algorithm is used to locate key components of the fastener. Then, the geometric parameter of the fastener is worked out. An experiment was conducted on a high-speed railway near Wuhan, China to verify the accuracy and repeatability of the system. The maximum root-mean-square-error between the manual measurement and the system measurement is 0.3 mm, which demonstrates adequate accuracy. This system can replace manual measurements and greatly improve the efficiency of geometric parameter measurements for fasteners.
关键词： fastener geometric parameter measurement,region grow,high-speed railway,dense point cloud,structured light sensor
更新于2025-09-23 15:21:21