scholarly journals Detection and Modeling of Unstructured Roads in Forest Areas Based on Visual-2D Lidar Data Fusion

Forests ◽  
2021 ◽  
Vol 12 (7) ◽  
pp. 820
Author(s):  
Guannan Lei ◽  
Ruting Yao ◽  
Yandong Zhao ◽  
Yili Zheng
Keyword(s):  
Support Vector ◽  
Point Cloud Data ◽  
Recognition Method ◽  
Cloud Data ◽  
The Road ◽  
Nursery Stock ◽  
Time Requirements ◽  
Manual Signs ◽  
Forest Engineering ◽  
Detection And Recognition

The detection and recognition of unstructured roads in forest environments are critical for smart forestry technology. Forest roads lack effective reference objects and manual signs and have high degrees of nonlinearity and uncertainty, which pose severe challenges to forest engineering vehicles. This research aims to improve the automation and intelligence of forestry engineering and proposes an unstructured road detection and recognition method based on a combination of image processing and 2D lidar detection. This method uses the “improved SEEDS + Support Vector Machine (SVM)” strategy to quickly classify and recognize the road area in the image. Combined with the remapping of 2D lidar point cloud data on the image, the actual navigation requirements of forest unmanned navigation vehicles were fully considered, and road model construction based on the vehicle coordinate system was achieved. The algorithm was transplanted to a self-built intelligent navigation platform to verify its feasibility and effectiveness. The experimental results show that under low-speed conditions, the system can meet the real-time requirements of processing data at an average of 10 frames/s. For the centerline of the road model, the matching error between the image and lidar is no more than 0.119 m. The algorithm can provide effective support for the identification of unstructured roads in forest areas. This technology has important application value for forestry engineering vehicles in autonomous inspection and spraying, nursery stock harvesting, skidding, and transportation.

scholarly journals Rockfall Risk Analysis Based on Precise Topography using UAV

2020 ◽  
Vol 20 (4) ◽  
pp. 63-73
Author(s):  
Jaehee Choi ◽  
Namgyun Kim ◽  
Bongjin Choe ◽  
Byonghee Jun
Keyword(s):  
Point Cloud Data ◽  
University Campus ◽  
Digital Map ◽  
Cloud Data ◽  
The Road ◽  
Mountainous Regions ◽  
Digital Elevation ◽  
Surface Models ◽  
Aerial Vehicle ◽  
Elevation Model

In this study, the risk of rockfall on incision slopes adjacent to roads was evaluated using the RocFall program. The study area was a slope adjacent to the road leading to a university campus in Samcheok-si, Gangwon-do, with an area of 774 m<sup>2</sup> and an average slope of approximately 43°. A rock shed was installed at the lower zone of the slope. A 3D model of the terrain was generated based on point cloud data gathered using a UAV (unmanned aerial vehicle). Fast and accurate orthoimages were captured by UAV and high-resolution digital surface models (DSMs) were produced; these data were used to assess the risk of rockfall. Compared to terrain extraction using a digital elevation model (DEM) generated from an existing digital map, terrain extraction using a UAV was more effective in deriving results close to the actual situation in the field, especially for the analysis of rockfall jump height and kinetic energy. The necessity of constructing 3D topographic data using UAVs to predict rockfall disasters in mountainous regions was confirmed.

scholarly journals CLASSIFICATION OF LIDAR DATA FOR GENERATING A HIGH-PRECISION ROADWAY MAP

2016 ◽  
Vol XLI-B3 ◽  
pp. 251-254 ◽  
Author(s):  
J. Jeong ◽  
I. Lee
Keyword(s):  
Machine Learning ◽  
Point Cloud ◽  
Three Dimensional ◽  
Autonomous Driving ◽  
Learning Objects ◽  
Support Vector ◽  
Feature Descriptor ◽  
Point Cloud Data ◽  
Cloud Data ◽  
On The Road

Generating of a highly precise map grows up with development of autonomous driving vehicles. The highly precise map includes a precision of centimetres level unlike an existing commercial map with the precision of meters level. It is important to understand road environments and make a decision for autonomous driving since a robust localization is one of the critical challenges for the autonomous driving car. The one of source data is from a Lidar because it provides highly dense point cloud data with three dimensional position, intensities and ranges from the sensor to target. In this paper, we focus on how to segment point cloud data from a Lidar on a vehicle and classify objects on the road for the highly precise map. In particular, we propose the combination with a feature descriptor and a classification algorithm in machine learning. Objects can be distinguish by geometrical features based on a surface normal of each point. To achieve correct classification using limited point cloud data sets, a Support Vector Machine algorithm in machine learning are used. Final step is to evaluate accuracies of obtained results by comparing them to reference data The results show sufficient accuracy and it will be utilized to generate a highly precise road map.

scholarly journals VOXEL-BASED EXTRACTION OF INDIVIDUAL PYLONS AND WIRES FROM LIDAR POINT CLOUD DATA

2019 ◽  
Vol IV-4/W8 ◽  
pp. 91-98 ◽  
Author(s):  
N. Munir ◽  
M. Awrangjeb ◽  
B. Stantic ◽  
G. Lu ◽  
S. Islam
Keyword(s):  
Vertical Profile ◽  
Point Cloud ◽  
Support Vector ◽  
Svm Classifier ◽  
Lidar Data ◽  
Point Cloud Data ◽  
Cloud Data ◽  
3D Objects ◽  
Voxel Grid ◽  
Power Line Corridor

<p><strong>Abstract.</strong> Extraction of individual pylons and wires is important for modelling of 3D objects in a power line corridor (PLC) map. However, the existing methods mostly classify points into distinct classes like pylons and wires, but hardly into individual pylons or wires. The proposed method extracts standalone pylons, vegetation and wires from LiDAR data. The extraction of individual objects is needed for a detailed PLC mapping. The proposed approach starts off with the separation of ground and non ground points. The non-ground points are then classified into vertical (e.g., pylons and vegetation) and non-vertical (e.g., wires) object points using the vertical profile feature (VPF) through the binary support vector machine (SVM) classifier. Individual pylons and vegetation are then separated using their shape and area properties. The locations of pylons are further used to extract the span points between two successive pylons. Finally, span points are voxelised and alignment properties of wires in the voxel grid is used to extract individual wires points. The results are evaluated on dataset which has multiple spans with bundled wires in each span. The evaluation results show that the proposed method and features are very effective for extraction of individual wires, pylons and vegetation with 99% correctness and 98% completeness.</p>

Obstacle detection and recognition in farmland based on fusion point cloud data

2021 ◽  
Vol 189 ◽  
pp. 106409
Author(s):  
Yuhan Ji ◽  
Shichao Li ◽  
Cheng Peng ◽  
Hongzhen Xu ◽  
Ruyue Cao ◽  
...  
Keyword(s):  
Point Cloud ◽  
Obstacle Detection ◽  
Point Cloud Data ◽  
Cloud Data ◽  
Fusion Point ◽  
Detection And Recognition

Point Cloud Reconstruction Based on SVR

2011 ◽  
Vol 403-408 ◽  
pp. 3267-3270
Author(s):  
Jin Guang Sun ◽  
Jun Tao Wang ◽  
Xin Nian Yang ◽  
Yang Li
Keyword(s):  
Support Vector Regression ◽  
Point Cloud ◽  
Reconstruction Algorithm ◽  
Surface Expression ◽  
Experimental Results ◽  
Support Vector ◽  
Implicit Function ◽  
Point Cloud Data ◽  
Cloud Data ◽  
Marching Cube Algorithm

This paper presents a point cloud reconstruction algorithm which based on SVR(support vector regression) . Firstly, the point cloud data pre-processing, filter out noise points. Then train the point by SVR , and we can get the function of surface expression. Finally, using the Marching Cube algorithm to visualize the implicit function. Experimental results show that the algorithm is more robust and more efficient.

Research on Road Target Recognition Method Based on Optical Image

2014 ◽  
Vol 602-605 ◽  
pp. 1968-1971
Author(s):  
Man Zhao ◽  
Jin Jiang Cui ◽  
Fei Guo ◽  
Mei Zhao ◽  
Da Yong Jiang
Keyword(s):  
Target Recognition ◽  
Recognition System ◽  
Optical Image ◽  
Recognition Method ◽  
C Language ◽  
The Road ◽  
Optical Images ◽  
Target Extraction ◽  
Very High ◽  
Detection And Recognition

With the development of science and technology, optical images with very high resolution have been able to provide a large amount of information. Therein the road target is the most widely used in optical image. Road target detection and recognition is extremely important for reducing a lot of practical work and greatly improving the efficiency of the target extraction and identification. Aimed at this problem, we propose a road target recognition method based on optical image.The method is realized by joining human recognize and identify, combining with the intelligence of computer processing and powerful place. So in this work, the method based on edge detection and Hough transform algorithm is exploded. The man-machine interactive recognition system (Road Target Extraction and Recognition System) is developed. The system is realized under Windows operating system. The tool is Visual C++ 6.0 software. The platform is MFC functions. The system is written in C++ language. The characteristics of the system are the strong pertinence and the simple operation. When the system is applied safely, the results are definite and clear.

scholarly journals AUTOMATIC EXTRACTION OF ROAD MARKINGS FROM MOBILE LASER-POINT CLOUD USING INTENSITY DATA

2018 ◽  
Vol XLII-3 ◽  
pp. 2113-2119 ◽  
Author(s):  
L. Yao ◽  
Q. Chen ◽  
C. Qin ◽  
H. Wu ◽  
S. Zhang
Keyword(s):  
Template Matching ◽  
Point Cloud ◽  
Binary Image ◽  
Point Cloud Data ◽  
Classification Rate ◽  
Cloud Data ◽  
The Road ◽  
Distance Weighted ◽  
Mobile Mapping System ◽  
Road Marking

With the development of intelligent transportation, road’s high precision information data has been widely applied in many fields. This paper proposes a concise and practical way to extract road marking information from point cloud data collected by mobile mapping system (MMS). The method contains three steps. Firstly, road surface is segmented through edge detection from scan lines. Then the intensity image is generated by inverse distance weighted (IDW) interpolation and the road marking is extracted by using adaptive threshold segmentation based on integral image without intensity calibration. Moreover, the noise is reduced by removing a small number of plaque pixels from binary image. Finally, point cloud mapped from binary image is clustered into marking objects according to Euclidean distance, and using a series of algorithms including template matching and feature attribute filtering for the classification of linear markings, arrow markings and guidelines. Through processing the point cloud data collected by RIEGL VUX-1 in case area, the results show that the F-score of marking extraction is 0.83, and the average classification rate is 0.9.

scholarly journals Point Cloud Data Retrieval from 3D Geospatial Database for Automated Road Median Extraction

2018 ◽  
Author(s):  
Pankaj Kumar ◽  
Paul Lewis ◽  
Conor P. McElhinney
Keyword(s):  
Data Processing ◽  
Point Cloud ◽  
Laser Scanning ◽  
Data Retrieval ◽  
Lidar Data ◽  
Point Cloud Data ◽  
Cloud Data ◽  
The Road ◽  
Efficient Storage ◽  
Geospatial Database

Laser scanning systems make use of Light Detection and Ranging (LiDAR) technology to acquire accurately georeferenced sets of dense 3D point cloud data. The information acquired using these systems produces better knowledge about the terrain objects which are inherently 3D in nature. The LiDAR data acquired from mobile, airborne or terrestrial platforms provides several benefit over conventional sources of data acquisition in terms of accuracy, resolution and attributes. However, the large volume and scale of LiDAR data have inhibited the development of automated feature extraction algorithms due to the extensive computational cost involved in it. Moreover, the heterogeneously distributed point cloud, which represents objects with varying size, point density, holes and complicated structures pose a great challenge for data processing. Currently, geospatial database systems do not provide a robust solution for efficient storage and accessibility of raw data in a way that data processing could be applied based on optimal spatial extent. In this paper, we present Global LiDAR and Imagery Mobile Processing Spatial Environment (GLIMPSE) system that provides a framework for storage, management and integration of 3D LiDAR data acquired from multiple platforms. The system facilitates an efficient accessibility to the raw dataset, which is hierarchically represented in a geographically meaningful way. We utilise the GLIMPSE system to automatically extract road median from Airborne Laser Scanning (ALS) point cloud. In the first part of this paper, we detail an approach to efficiently retrieve the point cloud data from the GLIMPSE system for a particular geographic area based on user requirements. In the second part, we present an algorithm to automatically extract road median from the retrieved LiDAR data. The developed road median extraction algorithm utilises the LiDAR elevation and intensity attributes to distinguish the median from the road surface. We successfully tested our algorithms on two road sections consisting of distinct road median types based on concrete and grass-hedge barriers. The use of GLIMPSE improved the efficiency of the road median extraction in terms of fast accessibility to ALS point cloud data for the required road sections. The developed system and its associated algorithms provide a comprehensive solution to the user's requirement for an efficient storage, integration, retrieval and processing of large volumes of LiDAR point cloud data. These findings and knowledge contribute to a more rapid, cost-effective and comprehensive approach to surveying road networks.

scholarly journals CLASSIFICATION OF LIDAR DATA FOR GENERATING A HIGH-PRECISION ROADWAY MAP

2016 ◽  
Vol XLI-B3 ◽  
pp. 251-254
Author(s):  
J. Jeong ◽  
I. Lee
Keyword(s):  
Machine Learning ◽  
Point Cloud ◽  
Three Dimensional ◽  
Autonomous Driving ◽  
Learning Objects ◽  
Support Vector ◽  
Feature Descriptor ◽  
Point Cloud Data ◽  
Cloud Data ◽  
On The Road

Generating of a highly precise map grows up with development of autonomous driving vehicles. The highly precise map includes a precision of centimetres level unlike an existing commercial map with the precision of meters level. It is important to understand road environments and make a decision for autonomous driving since a robust localization is one of the critical challenges for the autonomous driving car. The one of source data is from a Lidar because it provides highly dense point cloud data with three dimensional position, intensities and ranges from the sensor to target. In this paper, we focus on how to segment point cloud data from a Lidar on a vehicle and classify objects on the road for the highly precise map. In particular, we propose the combination with a feature descriptor and a classification algorithm in machine learning. Objects can be distinguish by geometrical features based on a surface normal of each point. To achieve correct classification using limited point cloud data sets, a Support Vector Machine algorithm in machine learning are used. Final step is to evaluate accuracies of obtained results by comparing them to reference data The results show sufficient accuracy and it will be utilized to generate a highly precise road map.

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