scholarly journals Accurate Road Marking Detection from Noisy Point Clouds Acquired by Low-Cost Mobile LiDAR Systems

2020 ◽  
Vol 9 (10) ◽  
pp. 608
Author(s):  
Ronghao Yang ◽  
Qitao Li ◽  
Junxiang Tan ◽  
Shaoda Li ◽  
Xinyu Chen
Keyword(s):  
Low Cost ◽  
Point Clouds ◽  
The Road ◽  
Online Processing ◽  
Beam Laser ◽  
Intensity Information ◽  
Laser Scanners ◽  
Quantitative Results ◽  
Scan Line ◽  
Road Marking

Road markings that provide instructions for unmanned driving are important elements in high-precision maps. In road information collection technology, multi-beam mobile LiDAR scanning (MLS) is currently adopted instead of traditional mono-beam LiDAR scanning because of the advantages of low cost and multiple fields of view for multi-beam laser scanners; however, the intensity information scanned by multi-beam systems is noisy and current methods designed for road marking detection from mono-beam point clouds are of low accuracy. This paper presents an accurate algorithm for detecting road markings from noisy point clouds, where most nonroad points are removed and the remaining points are organized into a set of consecutive pseudo-scan lines for parallel and/or online processing. The road surface is precisely extracted by a moving fitting window filter from each pseudo-scan line, and a marker edge detector combining an intensity gradient with an intensity statistics histogram is presented for road marking detection. Quantitative results indicate that the proposed method achieves average recall, precision, and Matthews correlation coefficient (MCC) levels of 90%, 95%, and 92%, respectively, showing excellent performance for road marking detection from multi-beam scanning point clouds.

scholarly journals Scan Line Based Road Marking Extraction from Mobile LiDAR Point Clouds

Sensors ◽  
2016 ◽  
Vol 16 (6) ◽  
pp. 903 ◽  
Author(s):  
Li Yan ◽  
Hua Liu ◽  
Junxiang Tan ◽  
Zan Li ◽  
Hong Xie ◽  
...  
Keyword(s):  
Point Clouds ◽  
Scan Line ◽  
Road Marking

scholarly journals Assessing Low-Cost Terrestrial Laser Scanners for Deriving Forest Structure Parameters

2021 ◽  
Author(s):  
Atticus E. L. Stovall ◽  
Jeff W Atkins
Keyword(s):  
Structural Parameters ◽  
Low Cost ◽  
Laser Scanner ◽  
Point Clouds ◽  
Model Reconstruction ◽  
Tree Model ◽  
Factors Affecting ◽  
Laser Scanners ◽  
Tree Models ◽  
Profile Reconstruction

The increasingly affordable price point of terrestrial laser scanners has led to a democratization of instrument availability, but the most common low-cost instruments have yet to be compared in terms of the consistency to measure forest structural attributes. Here, we compared two low-cost terrestrial laser scanners (TLS): the Leica BLK360 and the Faro Focus 120 3D. We evaluate the instruments in terms of point cloud quality, forest inventory estimates, tree-model reconstruction, and foliage profile reconstruction. Our direct comparison of the point clouds showed reduced noise in filtered Leica data. Tree diameter and height were consistent across instruments (4.4% and 1.4% error, respectively). Volumetric tree models were less consistent across instruments, with ~29% bias, depending on model reconstruction quality. In the process of comparing foliage profiles, we conducted a sensitivity analysis of factors affecting foliage profile estimates, showing a minimal effect from instrument maximum range (for forests less than ~50 m in height) and surprisingly little impact from degraded scan resolution. Filtered unstructured TLS point clouds must be artificially re-gridded to provide accurate foliage profiles. The factors evaluated in this comparison point towards necessary considerations for future low-cost laser scanner development and application in detecting forest structural parameters.

scholarly journals 3D SUPER-RESOLUTION APPROACH FOR SPARSE LASER SCANNER DATA

2015 ◽  
Vol II-3/W5 ◽  
pp. 151-157 ◽  
Author(s):  
S. Hosseinyalamdary ◽  
A. Yilmaz
Keyword(s):  
Anisotropic Diffusion ◽  
Low Cost ◽  
Laser Scanner ◽  
Super Resolution ◽  
Point Clouds ◽  
Superior Performance ◽  
Laser Scanners ◽  
Anisotropic Diffusion Equation ◽  
Tracking Object ◽  
High Level

Laser scanner point cloud has been emerging in Photogrammetry and computer vision to achieve high level tasks such as object tracking, object recognition and scene understanding. However, low cost laser scanners are noisy, sparse and prone to systematic errors. This paper proposes a novel 3D super resolution approach to reconstruct surface of the objects in the scene. This method works on sparse, unorganized point clouds and has superior performance over other surface recovery approaches. Since the proposed approach uses anisotropic diffusion equation, it does not deteriorate the object boundaries and it preserves topology of the object.

scholarly journals AUTOMATIC ROAD MARKINGS EXTRACTION, CLASSIFICATION AND VECTORIZATION FROM MOBILE LASER SCANNING DATA

2019 ◽  
Vol XLII-2/W13 ◽  
pp. 1089-1096
Author(s):  
Y. Pan ◽  
B. Yang ◽  
S. Li ◽  
H. Yang ◽  
Z. Dong ◽  
...  
Keyword(s):  
Laser Scanning ◽  
Point Clouds ◽  
Unmanned Vehicles ◽  
Model Matching ◽  
High Definition ◽  
Processing Scheme ◽  
The Road ◽  
Reference Images ◽  
Road Marking ◽  
Boundary Lines

<p><strong>Abstract.</strong> To meet the demands of various applications such as high definition navigation map production for unmanned vehicles and road reconstruction and expansion engineering, this paper proposes an effective and efficient approach to automatically extract, classify and vectorize road markings from Mobile Laser Scanning (MLS) point clouds. Firstly, the MLS point cloud is segmented to ground and non-ground points. Secondly, several geo-reference images are generated and further used to detect road markings pixels under an image processing scheme. Thirdly, road marking point clouds are retrieved from the image and further segmented into connected objects. Otsu thresholding and Statistic Outlier Remover are adopted to refine the road marking objects. Next, each road marking objects are classified into several categories such as boundary lines, rectangle road markings, etc. based on its bounding box information. Other irregular road markings are classified by a model matching scheme. Finally, all classified road markings are vectorized as closed or unclosed polylines after reconnecting the breaking boundary lines. Comprehensive experiments are done on various MLS point clouds of both the urban and highway scenarios, which show that the precision and recall of the proposed method is higher than 95% for road marking extraction and as high as 93% for road marking classification on highway scenarios. The ratio is 92% and 85% for urban scenarios.</p>

scholarly journals Automatic Road Marking Extraction and Vectorization from Vehicle-Borne Laser Scanning Data

2021 ◽  
Vol 13 (13) ◽  
pp. 2612
Author(s):  
Lianbi Yao ◽  
Changcai Qin ◽  
Qichao Chen ◽  
Hangbin Wu
Keyword(s):  
High Precision ◽  
Point Cloud ◽  
Point Clouds ◽  
Adaptive Threshold ◽  
Point Cloud Data ◽  
Threshold Segmentation ◽  
Cloud Data ◽  
The Road ◽  
Automatic Driving ◽  
Road Marking

Automatic driving technology is becoming one of the main areas of development for future intelligent transportation systems. The high-precision map, which is an important supplement of the on-board sensors during shielding or limited observation distance, provides a priori information for high-precision positioning and path planning in automatic driving. The position and semantic information of the road markings, such as absolute coordinates of the solid lines and dashed lines, are the basic components of the high-precision map. In this paper, we study the automatic extraction and vectorization of road markings. Firstly, scan lines are extracted from the vehicle-borne laser point cloud data, and the pavement is extracted from scan lines according to the geometric mutation at the road boundary. On this basis, the pavement point clouds are transformed into raster images with a certain resolution by using the method of inverse distance weighted interpolation. An adaptive threshold segmentation algorithm is used to convert raster images into binary images. Followed by the adaptive threshold segmentation is the Euclidean clustering method, which is used to extract road markings point clouds from the binary image. Solid lines are detected by feature attribute filtering. All of the solid lines and guidelines in the sample data are correctly identified. The deep learning network framework PointNet++ is used for semantic recognition of the remaining road markings, including dashed lines, guidelines and arrows. Finally, the vectorization of the identified solid lines and dashed lines is carried out based on a line segmentation self-growth algorithm. The vectorization of the identified guidelines is carried out according to an alpha shape algorithm. Point cloud data from four experimental areas are used for road marking extraction and identification. The F-scores of the identification of dashed lines, guidelines, straight arrows and right turn arrows are 0.97, 0.66, 0.84 and 1, respectively.

Development, Calibration and Evaluation of a Portable and Direct Georeferenced Laser Scanning System for Kinematic 3D Mapping

2015 ◽  
Vol 9 (4) ◽  
Author(s):  
Erik Heinz ◽  
Christian Eling ◽  
Markus Wieland ◽  
Lasse Klingbeil ◽  
Heiner Kuhlmann
Keyword(s):  
Laser Scanning ◽  
Attitude Determination ◽  
Low Cost ◽  
Laser Scanner ◽  
Point Clouds ◽  
Scanning System ◽  
Planar Surfaces ◽  
Laser Scanners ◽  
Scanning Geometry ◽  
Laser Scanning System

AbstractIn recent years, kinematic laser scanning has become increasingly popular because it offers many benefits compared to static laser scanning. The advantages include both saving of time in the georeferencing and a more favorable scanning geometry. Often mobile laser scanning systems are installed on wheeled platforms, which may not reach all parts of the object. Hence, there is an interest in the development of portable systems, which remain operational even in inaccessible areas. The development of such a portable laser scanning system is presented in this paper. It consists of a lightweight direct georeferencing unit for the position and attitude determination and a small low-cost 2D laser scanner. This setup provides advantages over existing portable systems that employ heavy and expensive 3D laser scanners in a profiling mode.A special emphasis is placed on the system calibration, i. e. the determination of the transformation between the coordinate frames of the direct georeferencing unit and the 2D laser scanner. To this end, a calibration field is used, which consists of differently orientated georeferenced planar surfaces, leading to estimates for the lever arms and boresight angles with an accuracy of mm and one-tenth of a degree. Finally, point clouds of the mobile laser scanning system are compared with georeferenced point clouds of a high-precision 3D laser scanner. Accordingly, the accuracy of the system is in the order of cm to dm. This is in good agreement with the expected accuracy, which has been derived from the error propagation of previously estimated variance components.

scholarly journals Semantic Interpretation of Mobile Laser Scanner Point Clouds in Indoor Scenes Using Trajectories

2018 ◽  
Vol 10 (11) ◽  
pp. 1754 ◽  
Author(s):  
Shayan Nikoohemat ◽  
Michael Peter ◽  
Sander Oude Elberink ◽  
George Vosselman
Keyword(s):  
3D Modeling ◽  
Point Cloud ◽  
Essential Role ◽  
Low Cost ◽  
Laser Scanner ◽  
Point Clouds ◽  
Semantic Interpretation ◽  
Adjacency Graph ◽  
Laser Scanners ◽  
Indoor Scenes

The data acquisition with Indoor Mobile Laser Scanners (IMLS) is quick, low-cost and accurate for indoor 3D modeling. Besides a point cloud, an IMLS also provides the trajectory of the mobile scanner. We analyze this trajectory jointly with the point cloud to support the labeling of noisy, highly reflected and cluttered points in indoor scenes. An adjacency-graph-based method is presented for detecting and labeling of permanent structures, such as walls, floors, ceilings, and stairs. Through occlusion reasoning and the use of the trajectory as a set of scanner positions, gaps are discriminated from real openings in the data. Furthermore, a voxel-based method is applied for labeling of navigable space and separating them from obstacles. The results show that 80% of the doors and 85% of the rooms are correctly detected, and most of the walls and openings are reconstructed. The experimental outcomes indicate that the trajectory of MLS systems plays an essential role in the understanding of indoor scenes.

scholarly journals Mobile 3D Mapping with a Low-Cost UAV-Based LIDAR System

2021 ◽  
pp. 108-116
Author(s):  
A.T.T. Phan ◽  
C.B.V. Dang ◽  
K. Takahashi
Keyword(s):  
Large Scale ◽  
Low Cost ◽  
Control Point ◽  
Laser Scanner ◽  
Point Clouds ◽  
Topographic Map ◽  
Experimental Site ◽  
Accuracy Requirement ◽  
Beam Laser ◽  
Flight Height

Recently, many UAVs (unmanned aerial vehicles) based on LiDAR (light detection and ranging) systems have been developed for various purpose because of the effective of LIDAR technique and low-cost UAV. In this study, the accuracy of point clouds generated by the developed for a low-cost UAV-based LiDAR systems is evaluated. The system consisting of a multi-beam laser scanner- Velodyne VLP 16 and DJI M600 UAV. The experimental site is undulation with less object in Nagaoka city, Niigata Prefecture, Japan Twelve reflectance makers are arranged as ground control point for the positioning evaluating process. The observed data was collected on Nov. 8th, 2019 with three different flight height at 10m, 20m and 30m. For generating the point clouds, the mounting parameters and sensor parameters are combined. The generated point clouds are corrected by applying bias correction and the 7 parameters transformation. The result is validated using three different experimental setups with three various flight height which indicate that the most accurate and reliable results are obtained. As a result, the point clouds after calibrating attained an accuracy of approximate 0.2 m in vertical and horizontal for both correction methods. In conclusion, the point cloud accuracy is not good enough for generating the topographic map at large scale. However, the stable results and the present accuracy are good for other purposes with less accuracy requirement such as monitoring the crop growth.

scholarly journals Evaluating the Safety Risk of Rural Roadsides Using a Bayesian Network Method

2019 ◽  
Vol 16 (7) ◽  
pp. 1166 ◽  
Author(s):  
Tianpei Tang ◽  
Senlai Zhu ◽  
Yuntao Guo ◽  
Xizhao Zhou ◽  
Yang Cao
Keyword(s):  
Bayesian Network ◽  
Low Cost ◽  
Road Networks ◽  
Access Point ◽  
Safety Risk ◽  
Crash Data ◽  
Risk Levels ◽  
The Road ◽  
Run Off ◽  
Point Density

Evaluating the safety risk of rural roadsides is critical for achieving reasonable allocation of a limited budget and avoiding excessive installation of safety facilities. To assess the safety risk of rural roadsides when the crash data are unavailable or missing, this study proposed a Bayesian Network (BN) method that uses the experts’ judgments on the conditional probability of different safety risk factors to evaluate the safety risk of rural roadsides. Eight factors were considered, including seven factors identified in the literature and a new factor named access point density. To validate the effectiveness of the proposed method, a case study was conducted using 19.42 km long road networks in the rural area of Nantong, China. By comparing the results of the proposed method and run-off-road (ROR) crash data from 2015–2016 in the study area, the road segments with higher safety risk levels identified by the proposed method were found to be statistically significantly correlated with higher crash severity based on the crash data. In addition, by comparing the respective results evaluated by eight factors and seven factors (a new factor removed), we also found that access point density significantly contributed to the safety risk of rural roadsides. These results show that the proposed method can be considered as a low-cost solution to evaluating the safety risk of rural roadsides with relatively high accuracy, especially for areas with large rural road networks and incomplete ROR crash data due to budget limitation, human errors, negligence, or inconsistent crash recordings.

scholarly journals Smart Collaborative Performance-Induced Parameter Identification Algorithms for Synchronous Reluctance Machine Magnetic Model

2021 ◽  
Vol 13 (8) ◽  
pp. 4379
Author(s):  
Linjie Ren ◽  
Guobin Lin ◽  
Yuanzhe Zhao ◽  
Zhiming Liao
Keyword(s):  
Low Cost ◽  
Parameter Extraction ◽  
Synchronous Reluctance Machine ◽  
Fitness Evaluation ◽  
Reluctance Machine ◽  
Candidate Solution ◽  
Collaborative Performance ◽  
Magnetic Model ◽  
Quantitative Results ◽  
Cost Characteristics

In rail transit traction, due to the remarkable energy-saving and low-cost characteristics, synchronous reluctance motors (SynRM) may be a potential substitute for traditional AC motors. However, in the parameter extraction of SynRM nonlinear magnetic model, the accuracy and robustness of the metaheuristic algorithm is restricted by the excessive dependence on fitness evaluation. In this paper, a novel probability-driven smart collaborative performance (SCP) is defined to quantify the comprehensive contribution of candidate solution in current population. With the quantitative results of SCP as feedback in-formation, an algorithm updating mechanism with improved evolutionary quality is established. The allocation of computing resources induced by SCP achieves a good balance between exploration and exploitation. Comprehensive experiment results demonstrate better effectiveness of SCP-induced algorithms to the proposed synchronous reluctance machine magnetic model. Accuracy and robustness of the proposed algorithms are ranked first in the comparison result statistics with other well-known algorithms.

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