scholarly journals BARRIER AND GUARDRAIL EXTRACTION AND CLASSIFICATION FROM POINT CLOUDS

2020 ◽  
Vol XLIII-B5-2020 ◽  
pp. 157-162
Author(s):  
M. Vidal ◽  
L. Díaz-Vilariño ◽  
P. Arias ◽  
J. Balado
Keyword(s):  
Traffic Safety ◽  
Autonomous Vehicles ◽  
Global Economy ◽  
Laser Scanner ◽  
Point Clouds ◽  
High Definition ◽  
Simple Method ◽  
Concrete Barriers ◽  
Parameter Dependent

Abstract. In the recent years, the modelling of infrastructures has been receiving increasingly attention due to the importance of transport infrastructures for global economy, traffic safety and for the generation of high definition maps, essential to autonomous vehicles. This paper presents a simple method for the segmentation and classification of concrete barriers and guardrails in road surroundings. First steps of the method are aimed to delimit the region of the point cloud outside the driving lanes in which barriers and guardrails are installed. The purpose is to significantly reduce the size of point clouds in order to improve further processing. Then, barrier segmentation and classification are designed as parameter-dependent processes because the geometric features of roads and barriers and guardrails are mostly regulated by norms and standards. Results show a good performance in terms of classification in comparison of other state of the art methods. Better results were obtained for guardrails than for concrete barriers. The method has been tested in a set of point clouds acquired with a Mobile Laser Scanner from conventional roads and highways.

scholarly journals Segmentation and Multi-Scale Convolutional Neural Network-Based Classification of Airborne Laser Scanner Data

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3347 ◽  
Author(s):  
Zhishuang Yang ◽  
Bo Tan ◽  
Huikun Pei ◽  
Wanshou Jiang
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Laser Scanning ◽  
Laser Scanner ◽  
Region Growing ◽  
Point Clouds ◽  
Classification Method ◽  
Multi Scale ◽  
Airborne Laser

The classification of point clouds is a basic task in airborne laser scanning (ALS) point cloud processing. It is quite a challenge when facing complex observed scenes and irregular point distributions. In order to reduce the computational burden of the point-based classification method and improve the classification accuracy, we present a segmentation and multi-scale convolutional neural network-based classification method. Firstly, a three-step region-growing segmentation method was proposed to reduce both under-segmentation and over-segmentation. Then, a feature image generation method was used to transform the 3D neighborhood features of a point into a 2D image. Finally, feature images were treated as the input of a multi-scale convolutional neural network for training and testing tasks. In order to obtain performance comparisons with existing approaches, we evaluated our framework using the International Society for Photogrammetry and Remote Sensing Working Groups II/4 (ISPRS WG II/4) 3D labeling benchmark tests. The experiment result, which achieved 84.9% overall accuracy and 69.2% of average F1 scores, has a satisfactory performance over all participating approaches analyzed.

scholarly journals Design and Implementation of Edge-Fog-Cloud System through HD Map Generation from LiDAR Data of Autonomous Vehicles

Electronics ◽  
2020 ◽  
Vol 9 (12) ◽  
pp. 2084
Author(s):  
Junwon Lee ◽  
Kieun Lee ◽  
Aelee Yoo ◽  
Changjoo Moon
Keyword(s):  
Real Time ◽  
Autonomous Vehicles ◽  
Fog Computing ◽  
Point Clouds ◽  
Lidar Data ◽  
High Definition ◽  
Localization And Mapping ◽  
Cloud Server ◽  
Map Generation ◽  
Self Driving Cars

Self-driving cars, autonomous vehicles (AVs), and connected cars combine the Internet of Things (IoT) and automobile technologies, thus contributing to the development of society. However, processing the big data generated by AVs is a challenge due to overloading issues. Additionally, near real-time/real-time IoT services play a significant role in vehicle safety. Therefore, the architecture of an IoT system that collects and processes data, and provides services for vehicle driving, is an important consideration. In this study, we propose a fog computing server model that generates a high-definition (HD) map using light detection and ranging (LiDAR) data generated from an AV. The driving vehicle edge node transmits the LiDAR point cloud information to the fog server through a wireless network. The fog server generates an HD map by applying the Normal Distribution Transform-Simultaneous Localization and Mapping(NDT-SLAM) algorithm to the point clouds transmitted from the multiple edge nodes. Subsequently, the coordinate information of the HD map generated in the sensor frame is converted to the coordinate information of the global frame and transmitted to the cloud server. Then, the cloud server creates an HD map by integrating the collected point clouds using coordinate information.

scholarly journals COMPARISON ASSESSMENT OF DIGITAL 3D MODELS OBTAINED BY DRONE-BASED LIDAR AND DRONE IMAGERY

2021 ◽  
Vol XLVI-4/W5-2021 ◽  
pp. 113-118
Author(s):  
M. Bouziani ◽  
M. Amraoui ◽  
S. Kellouch
Keyword(s):  
Laser Scanner ◽  
Digital Terrain Model ◽  
Point Clouds ◽  
3D Models ◽  
Pulse Frequency ◽  
Airborne Lidar ◽  
Measurement Unit ◽  
Surface Model ◽  
Digital Products

Abstract. The purpose of this study is to assess the potential of drone airborne LiDAR technology in Morocco in comparison with drone photogrammetry. The cost and complexity of the equipment which includes a laser scanner, an inertial measurement unit, a positioning system and a platform are among the causes limiting its use. Furthermore, this study was motivated by the following reasons: (1) Limited number of studies in Morocco on drone-based LiDAR technology applications, (2) Lack of study on the parameters that influence the quality of drone-based LiDAR surveys as well as on the evaluation of the accuracy of derived products. In this study, the evaluation of LiDAR technology was carried out by an analysis of the geometric accuracy of the 3D products generated: Digital Terrain Model (DTM), Digital Surface Model (DSM) and Digital Canopy Model (DCM). We conduct a comparison with the products generated by drone photogrammetry and GNSS surveys. Several tests were carried out to analyse the parameters that influence the mission results namely height, overlap, drone speed and laser pulse frequency. After data collection, the processing phase was carried out. It includes: the cleaning, the consolidation then the classification of point clouds and the generation of the various digital models. This project also made it possible to propose and validate a workflow for the processing, the classification of point clouds and the generation of 3D digital products derived from the processing of LiDAR data acquired by drone.

scholarly journals AUTOMATIC CLASSIFICATION OF BRIDGES AND CONTINENTAL WATER BODIES FROM 3D POINT CLOUDS (AERIAL LIDAR)

2019 ◽  
Vol XLII-2/W13 ◽  
pp. 1047-1051
Author(s):  
S. Lorite Martínez ◽  
J. Moreno Jabato ◽  
J. M. Garrido Sáenz de Tejada ◽  
B. Rodríguez-Cuenca
Keyword(s):  
Laser Scanner ◽  
Point Clouds ◽  
Automatic Classification ◽  
Water Bodies ◽  
Main Process ◽  
Area Of Interest ◽  
Water Detection ◽  
3D Point Clouds ◽  
Continental Water Bodies

<p><strong>Abstract.</strong> The use of algorithms for automatic classification of aerial laser scanner 3D Point Clouds is the main process that improves its thematic quality. The main objectives of using 3D Point Clouds are the description of the surface and the detection of objects. The aim of this proposal for bridge and water detection algorithms is to increase the range and accuracy of the classification parameters of these products obtained with LiDAR technologies. With this methodology, the Digital Elevation Models (DEM) quality is improved and they are obtained by automated models of bridges and hydrography.</p><p>This paper describes a methodology to detect and classify bridges and continental water bodies in points using the properties of LiDAR technology such as radiometric and geometric variables implementing indexes like NDVI, NDWI or NFC. In addition, the Network of Roads and Hydrographic models in Spain are used to reduce the area of interest and errors. Part of the province of Teruel (Spain) has been used as study area.</p>

scholarly journals CLASSIFICATION OF POLE-LIKE OBJECTS USING POINT CLOUDS AND IMAGES CAPTURED BY MOBILE MAPPING SYSTEMS

2018 ◽  
Vol XLII-2 ◽  
pp. 731-738 ◽  
Author(s):  
Y. Mori ◽  
K. Kohira ◽  
H. Masuda
Keyword(s):  
Laser Scanner ◽  
Point Clouds ◽  
Target Object ◽  
Mobile Mapping ◽  
The Third ◽  
Mapping System ◽  
Mobile Mapping System ◽  
Feature Values ◽  
3D Shapes

The vehicle-based mobile mapping system (MMS) is effective for capturing 3D shapes and images of roadside objects. The laser scanner and cameras on the MMS capture point-clouds and sequential digital images synchronously during driving. In this paper, we propose a method for detecting and classifying pole-like objects using both point-clouds and images captured using the MMS. In our method, pole-like objects are detected from point-clouds, and then target objects, which are objects attached to poles, are extracted for identifying the types of pole-like objects. For associating each target object with images, the points of the target object are projected onto images, and the image of the target object is cropped. Each pole-like object is represented as a feature vector, which are calculated from point-clouds and images. The feature values of a point-cloud are calculated by point processing, and the ones of the cropped image are calculated using a convolutional neural network. The feature values of point-clouds and images are unified, and they are used as the input to machine learning. In experiments, we classified pole-like objects using three methods. The first method used only point-clouds, the second used only images, and the third used both point-clouds and images. The experimental results showed that the third method could most accurately classify pole-like objects.

scholarly journals EXTRACTION OF ROAD EDGES FROM MLS POINT CLOUDS USING BEND ANGLE OF SCANLINES

2020 ◽  
Vol XLIII-B2-2020 ◽  
pp. 1091-1097
Author(s):  
R. Honma ◽  
H. Date ◽  
S. Kanai
Keyword(s):  
Extraction Method ◽  
Laser Scanning ◽  
Computational Cost ◽  
Laser Scanner ◽  
Local Maximum ◽  
Point Clouds ◽  
Bend Angle ◽  
Edge Extraction ◽  
High Definition ◽  
Seed Points

Abstract. Efficient road edge extraction from point clouds acquired by Mobile Laser Scanning (MLS) is an important task because the road edge is one of the main elements of high definition maps. In this paper, we present a scanline-based road edge extraction method using a bend angle of scanlines from MLS point clouds. Scanline-based methods have advantages in that computational cost is low, it is easy to extract accurate road edges, and they are independent of driving speed of MLS compared to methods using unorganized points. In contrast, there are some problems with these methods where the extraction accuracy becomes low at curb cuts and intersections. The extraction accuracy becomes low caused by the scanning noise and small occlusion from weeds and fallen leaves. In addition, some parameters should be adjusted according to the mounting angle of the laser scanner on the vehicle. Therefore, we present a scanline-based road edge extraction method which can solve these problems. First, the points of the scanline are projected to a plane in order to reduce the influence of the mounting angle of the laser scanner on the vehicle. Next, the bend angle of each point is calculated by using filtered point clouds which are not vulnerable to small occlusions around the curb such as weeds. Then, points with a local maximum of bend angle and close to trajectories are extracted as seed points. Finally, road edges are generated by tracking based on bend angle of scanlines and smoothness of road edges from the seed points. In the experiments, our proposed methods achieved a completeness of over 95.3%, a correctness of over 95.0%, a quality of over 90.7%, and RMS difference less than 18.7 mm in total.

scholarly journals Spectral Pattern Classification in Lidar Data for Rock Identification in Outcrops

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Leonardo Campos Inocencio ◽  
Mauricio Roberto Veronez ◽  
Francisco Manoel Wohnrath Tognoli ◽  
Marcelo Kehl de Souza ◽  
Reginaldo Macedônio da Silva ◽  
...  
Keyword(s):  
Point Cloud ◽  
Laser Scanner ◽  
Point Clouds ◽  
Lidar Data ◽  
Spectral Signatures ◽  
Physical Chemical ◽  
Compositional Changes ◽  
Number Of Classes ◽  
Digital Outcrop

The present study aimed to develop and implement a method for detection and classification of spectral signatures in point clouds obtained from terrestrial laser scanner in order to identify the presence of different rocks in outcrops and to generate a digital outcrop model. To achieve this objective, a software based on cluster analysis was created, named K-Clouds. This software was developed through a partnership between UNISINOS and the company V3D. This tool was designed to begin with an analysis and interpretation of a histogram from a point cloud of the outcrop and subsequently indication of a number of classes provided by the user, to process the intensity return values. This classified information can then be interpreted by geologists, to provide a better understanding and identification from the existing rocks in the outcrop. Beyond the detection of different rocks, this work was able to detect small changes in the physical-chemical characteristics of the rocks, as they were caused by weathering or compositional changes.

scholarly journals Automatic Vector-Based Road Structure Mapping Using Multibeam LiDAR

2019 ◽  
Vol 11 (14) ◽  
pp. 1726 ◽  
Author(s):  
Junqiao Zhao ◽  
Xudong He ◽  
Jun Li ◽  
Tiantian Feng ◽  
Chen Ye ◽  
...  
Keyword(s):  
Autonomous Vehicles ◽  
Point Clouds ◽  
Mapping Method ◽  
Structure Mapping ◽  
High Definition ◽  
The Road ◽  
Planning And Control ◽  
Localization And Mapping ◽  
Efficient Vector ◽  
And Control

The high-definition map (HD-map) of road structures is crucial for the safe planning and control of autonomous vehicles. However, generating and updating such maps requires intensive manual work. Simultaneous localization and mapping (SLAM) is able to automatically build and update a map of the environment. Nevertheless, there is still a lack of SLAM method for generating vector-based road structure maps. In this paper, we propose a vector-based SLAM method for the road structure mapping using vehicle-mounted multibeam LiDAR. We propose using polylines as the primary mapping element instead of grid maps or point clouds because the vector-based representation is lightweight and precise. We explored the following: (1) the extraction and vectorization of road structures based on multiframe probabilistic fusion; (2) the efficient vector-based matching between frames of road structures; (3) the loop closure and optimization based on the pose-graph; and (4) the global reconstruction of the vector map. One specific road structure, the road boundary, is taken as an example. We applied the proposed mapping method to three road scenes, ranging from hundreds of meters to over ten kilometers and the results are automatically generated vector-based road boundary maps. The average absolute pose error of the trajectory in the mapping is 1.83 m without the aid of high-precision GPS.

scholarly journals High Definition 3D Map Creation Using GNSS/IMU/LiDAR Sensor Integration to Support Autonomous Vehicle Navigation

Sensors ◽  
2020 ◽  
Vol 20 (3) ◽  
pp. 899 ◽  
Author(s):  
Veli Ilci ◽  
Charles Toth
Keyword(s):  
Autonomous Vehicles ◽  
Moving Objects ◽  
Autonomous Vehicle ◽  
Point Clouds ◽  
Global Navigation Satellite Systems ◽  
Sensor Data ◽  
Measurement Unit ◽  
High Definition ◽  
Mobile Mapping ◽  
Autonomous Vehicle Navigation

Recent developments in sensor technologies such as Global Navigation Satellite Systems (GNSS), Inertial Measurement Unit (IMU), Light Detection and Ranging (LiDAR), radar, and camera have led to emerging state-of-the-art autonomous systems, such as driverless vehicles or UAS (Unmanned Airborne Systems) swarms. These technologies necessitate the use of accurate object space information about the physical environment around the platform. This information can be generally provided by the suitable selection of the sensors, including sensor types and capabilities, the number of sensors, and their spatial arrangement. Since all these sensor technologies have different error sources and characteristics, rigorous sensor modeling is needed to eliminate/mitigate errors to obtain an accurate, reliable, and robust integrated solution. Mobile mapping systems are very similar to autonomous vehicles in terms of being able to reconstruct the environment around the platforms. However, they differ a lot in operations and objectives. Mobile mapping vehicles use professional grade sensors, such as geodetic grade GNSS, tactical grade IMU, mobile LiDAR, and metric cameras, and the solution is created in post-processing. In contrast, autonomous vehicles use simple/inexpensive sensors, require real-time operations, and are primarily interested in identifying and tracking moving objects. In this study, the main objective was to assess the performance potential of autonomous vehicle sensor systems to obtain high-definition maps based on only using Velodyne sensor data for creating accurate point clouds. In other words, no other sensor data were considered in this investigation. The results have confirmed that cm-level accuracy can be achieved.

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