scholarly journals ROAD SURFACE DETECTION FROM MOBILE LIDAR DATA

2018 ◽  
Vol IV-5 ◽  
pp. 95-101 ◽  
Author(s):  
M. Yadav ◽  
B. Lohani ◽  
A. K. Singh
Keyword(s):  
Point Cloud ◽  
Three Dimensional ◽  
Test Site ◽  
Road Surface ◽  
Lidar Data ◽  
Rural Roads ◽  
The Road ◽  
Surface Detection ◽  
Rural Road ◽  
Data Points

<p><strong>Abstract.</strong> The accurate three-dimensional road surface information is highly useful for health assessment and maintenance of roads. It is basic information for further analysis in several applications including road surface settlement, pavement condition assessment and slope collapse. Mobile LiDAR system (MLS) is frequently used now a days to collect detail road surface and its surrounding information in terms three-dimensional (3D) point cloud. Extraction of road surface from volumetric point cloud data is still in infancy stage because of heavy data processing requirement and the complexity in the road environment. The extraction of roads especially rural road, where road-curb is not present is very tedious job especially in Indian roadway settings. Only a few studies are available, and none for Indian roads, in the literature for rural road detection. The limitations of existing studies are in terms of their lower accuracy, very slow speed of data processing and detection of other objects having similar characteristics as the road surface. A fast and accurate method is proposed for LiDAR data points of road surface detection, keeping in mind the essence of road surface extraction especially for Indian rural roads. The Mobile LiDAR data in <i>XYZI</i> format is used as input in the proposed method. First square gridding is performed and ground points are roughly extracted. Then planar surface detection using mathematical framework of principal component analysis (PCA) is performed and further road surface points are detected using similarity in intensity and height difference of road surface pointe in their neighbourhood.</p><p>A case study was performed on the MLS data points captured along wide-street (two-lane road without curb) of 156<span class="thinspace"></span>m length along rural roadway site in the outskirt of Bengaluru city (South-West of India). The proposed algorithm was implemented on the MLS data of test site and its performance was evaluated it terms of recall, precision and overall accuracy that were 95.27%, 98.85% and 94.23%, respectively. The algorithm was found computationally time efficient. A 7.6 million MLS data points of size 27.1<span class="thinspace"></span>MB from test site were processed in 24 minutes using the available computational resources. The proposed method is found to work even for worst case scenarios, i.e., complex road environments and rural roads, where road boundary is not clear and generally merged with road-side features.</p>

scholarly journals Mobile Laser Scanning Data for the Evaluation of Pavement Surface Distress

2020 ◽  
Vol 12 (6) ◽  
pp. 942 ◽  
Author(s):  
Maria Rosaria De Blasiis ◽  
Alessandro Di Benedetto ◽  
Margherita Fiani
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Road Traffic ◽  
Single Point ◽  
Three Dimensional ◽  
Point Clouds ◽  
Road Surface ◽  
Important Indicator ◽  
The Road ◽  
Laser Systems

The surface conditions of road pavements, including the occurrence and severity of distresses present on the surface, are an important indicator of pavement performance. Periodic monitoring and condition assessment is an essential requirement for the safety of vehicles moving on that road and the wellbeing of people. The traditional characterization of the different types of distress often involves complex activities, sometimes inefficient and risky, as they interfere with road traffic. The mobile laser systems (MLS) are now widely used to acquire detailed information about the road surface in terms of a three-dimensional point cloud. Despite its increasing use, there are still no standards for the acquisition and processing of the data collected. The aim of our work was to develop a procedure for processing the data acquired by MLS, in order to identify the localized degradations that mostly affect safety. We have studied the data flow and implemented several processing algorithms to identify and quantify a few types of distresses, namely potholes and swells/shoves, starting from very dense point clouds. We have implemented data processing in four steps: (i) editing of the point cloud to extract only the points belonging to the road surface, (ii) determination of the road roughness as deviation in height of every single point of the cloud with respect to the modeled road surface, (iii) segmentation of the distress (iv) computation of the main geometric parameters of the distress in order to classify it by severity levels. The results obtained by the proposed methodology are promising. The procedures implemented have made it possible to correctly segmented and identify the types of distress to be analyzed, in accordance with the on-site inspections. The tests carried out have shown that the choice of the values of some parameters to give as input to the software is not trivial: the choice of some of them is based on considerations related to the nature of the data, for others, it derives from the distress to be segmented. Due to the different possible configurations of the various distresses it is better to choose these parameters according to the boundary conditions and not to impose default values. The test involved a 100-m long urban road segment, the surface of which was measured with an MLS installed on a vehicle that traveled the road at 10 km/h.

scholarly journals EXTRACTION OF GROUND SURFACE ALONG ROADWAY FROM MOBILE LIDAR DATA

2018 ◽  
Vol IV-5 ◽  
pp. 103-107
Author(s):  
M. Yadav ◽  
A. K. Singh ◽  
B. Lohani
Keyword(s):  
Point Cloud ◽  
Total Error ◽  
Ground Surface ◽  
Test Site ◽  
Lidar Data ◽  
Type I ◽  
Digital Elevation ◽  
Data Points ◽  
Elevation Model ◽  
Road Widening

<p><strong>Abstract.</strong> High quality digital elevation model (DEM) is obtained from mobile LiDAR data and it is used in various applications like road widening, slope measurement of road side surfaces, and inundation of the roadway evaluation. Two steps algorithm is proposed to filter ground points using mobile LiDAR data. Initially unstructured input data is organized then standard deviation and flatness based approach is used to filter ground points. Proposed algorithm is tested on point cloud of test site located along 800<span class="thinspace"></span>m of roadway. Type I, Type II and total error are 2.11%, 2.21% and 2.15%, respectively with kappa is equal to 96.61% are computed using ground filtered points and reference data points.</p>

Handheld Laser Scanner Research

2019 ◽  
Vol 952 (10) ◽  
pp. 47-54
Author(s):  
A.V. Komissarov ◽  
A.V. Remizov ◽  
M.M. Shlyakhova ◽  
K.K. Yambaev
Keyword(s):  
Point Cloud ◽  
Three Dimensional ◽  
Laser Scanner ◽  
Test Site ◽  
Optical Systems ◽  
Advantages And Disadvantages ◽  
Site Survey ◽  
Laser Scanners ◽  
Three Dimensional Models ◽  
The Stability

The authors consider hand-held laser scanners, as a new photogrammetric tool for obtaining three-dimensional models of objects. The principle of their work and the newest optical systems based on various sensors measuring the depth of space are described in detail. The method of simultaneous navigation and mapping (SLAM) used for combining single scans into point cloud is outlined. The formulated tasks and methods for performing studies of the DotProduct (USA) hand-held laser scanner DPI?8X based on a test site survey are presented. The accuracy requirements for determining the coordinates of polygon points are given. The essence of the performed experimental research of the DPI?8X scanner is described, including scanning of a test object at various scanner distances, shooting a test polygon from various scanner positions and building point cloud, repeatedly shooting the same area of the polygon to check the stability of the scanner. The data on the assessment of accuracy and analysis of research results are given. Fields of applying hand-held laser scanners, their advantages and disadvantages are identified.

scholarly journals High-Resolution Representation for Mobile Mapping Data in Curved Regular Grid Model

Sensors ◽  
2019 ◽  
Vol 19 (24) ◽  
pp. 5373 ◽  
Author(s):  
Jingxin Su ◽  
Ryuji Miyazaki ◽  
Toru Tamaki ◽  
Kazufumi Kaneda
Keyword(s):  
High Resolution ◽  
Point Cloud ◽  
Cloud Model ◽  
Three Dimensional ◽  
Road Surface ◽  
Surface Elevation ◽  
Regular Grid ◽  
Real World Data ◽  
Mobile Mapping ◽  
Cloud Data

As mobile mapping systems become a mature technology, there are many applications for the process of the measured data. One interesting application is the use of driving simulators that can be used to analyze the data of tire vibration or vehicle simulations. In previous research, we presented our proposed method that can create a precise three-dimensional point cloud model of road surface regions and trajectory points. Our data sets were obtained by a vehicle-mounted mobile mapping system (MMS). The collected data were converted into point cloud data and color images. In this paper, we utilize the previous results as input data and present a solution that can generate an elevation grid for building an OpenCRG model. The OpenCRG project was originally developed to describe road surface elevation data, and also defined an open file format. As it can be difficult to generate a regular grid from point cloud directly, the road surface is first divided into straight lines, circular arcs, and and clothoids. Secondly, a non-regular grid which contains the elevation of road surface points is created for each road surface segment. Then, a regular grid is generated by accurately interpolating the elevation values from the non-regular grid. Finally, the curved regular grid (CRG) model files are created based on the above procedures, and can be visualized by OpenCRG tools. The experimental results on real-world data show that the proposed approach provided a very-high-resolution road surface elevation model.

Optimal Active Control of Vehicle Suspension System Including Time Delay and Preview for Rough Roads

2002 ◽  
Vol 8 (7) ◽  
pp. 967-991 ◽  
Author(s):  
Javad Marzbanrad ◽  
Goodarz Ahmadi ◽  
Yousef Hojjat ◽  
Hassan Zohoor
Keyword(s):  
Ride Comfort ◽  
Three Dimensional ◽  
Suspension System ◽  
Road Surface ◽  
Vehicle Suspension ◽  
Control Force ◽  
Preview Control ◽  
The Road ◽  
Road Surface Roughness ◽  
Vehicle Suspension System

An optimal preview control of a vehicle suspension system traveling on a rough road is studied. A three-dimensional seven degree-of-freedom car-riding model and several descriptions of the road surface roughness heights, including haversine (hole/bump) and stochastic filtered white noise models, are used in the analysis. It is assumed that contact-less sensors affixed to the vehicle front bumper measure the road surface height at some distances in the front of the car. The suspension systems are optimized with respect to ride comfort and road holding preferences including accelerations of the sprung mass, tire deflection, suspension rattle space and control force. The performance and power demand of active, active and delay, active and preview systems are evaluated and are compared with those for the passive system. The results show that the optimal preview control improves all aspects of the vehicle suspension performance while requiring less power. Effects of variation of preview time and variations in the road condition are also examined.

scholarly journals Low-Frequency Road Noise of Electric Vehicles Based on Measured Road Surface Morphology

2019 ◽  
Vol 10 (2) ◽  
pp. 33 ◽  
Author(s):  
Zhenqi Yu ◽  
Dong Cheng ◽  
Xingyuan Huang
Keyword(s):  
Surface Morphology ◽  
Simulation Analysis ◽  
Early Stage ◽  
Low Frequency ◽  
Processing System ◽  
Test Site ◽  
Road Surface ◽  
Labor Costs ◽  
Road Noise ◽  
The Road

In this paper, the noise vibration harshness (NVH) road surface morphology of a test site is scanned to establish a data processing system for the road surface, which can be used to transform the road surface morphology into the road surface excitation required for the road noise simulation analysis. The road surface morphology of the test site is used as the excitation input of the simulation analysis. The results obtained from the simulation analysis are equivalent to the experimental results. Using the actual scanning road surface morphology to simulate the excitation of a vehicle, the noise, as well as the vibration response of the vehicle under the actual road excitation of NVH in the early stage of vehicle development, can be accurately predicted. In the physical prototype stage, the rectification of vehicle road noise and the optimization to provide the needed excitation for the simulation analysis can be done, which will reduce the labor costs of the relevant experiment. Therefore, this method of road noise research has important engineering significance.

Versatile Model for Simulation of Rural Road Traffic

2005 ◽  
Vol 1934 (1) ◽  
pp. 168-178 ◽  
Author(s):  
Andreas Tapani
Keyword(s):  
Road Traffic ◽  
Pollutant Emissions ◽  
Driver Assistance Systems ◽  
Rural Roads ◽  
Detailed Model ◽  
Rural Highways ◽  
Urban Street ◽  
The Road ◽  
Rural Road ◽  
Microsimulation Models

In many countries the road mileage is dominated by rural highways. For that reason it is important to have access to efficient tools for evaluation of the performance of such roads. For other road types, e.g., freeways and urban street networks, a wealth of microsimulation models is available. However, only a few models dedicated to rural roads have been developed. None of these models handles traffic flows interrupted by intersections or roundabouts, nor are the models capable of describing the traffic flow on rural roads with a cable barrier between oncoming lanes. These are major drawbacks when Swedish roads, on which cable barriers and roundabouts are becoming increasingly important, are modeled. Moreover, as new areas of application for rural road simulation arise, a flexible and detailed model is needed. Such applications include, among other things, simulation of driver assistance systems and estimation of pollutant emissions. This paper introduces a versatile traffic microsimulation model for the rural roads of today and of the future. The model system presented, the Rural Traffic Simulator (RuTSim), is capable of handling all common types of rural roads, including the effects of roundabouts and intersections on the traffic on the main road. The purpose of the paper is to describe the simulation approach and the traffic modeling used in RuTSim. A verification of the RuTSim model is also included. RuTSim is found to produce outputs representative of all common types of rural roads in Sweden.

scholarly journals AUTOMATIC EXTRACTION AND RECOGNITION OF ROAD MARKINGS BASED ON VEHICLE LASER POINT CLOUD

2020 ◽  
Vol V-2-2020 ◽  
pp. 313-319
Author(s):  
L. Yao ◽  
C. Qin ◽  
Q. Chen ◽  
H. Wu ◽  
S. Zhang
Keyword(s):  
High Precision ◽  
Point Cloud ◽  
Vertical Section ◽  
Point Clouds ◽  
Binary Image ◽  
Road Surface ◽  
Surface Point ◽  
Buffer Area ◽  
The Road ◽  
Automatic Driving

Abstract. At present, automatic driving technology has become one of the development direction of the future intelligent transportation system. The high high-precision map, which is an important supplement of the on on-board sensors under the condition of shielding or the restriction of observation distance, provides a priori information for high high-precision positioning and path planning of the automatic driving with the level of L3 and above. The position and semantic information of the road markings, such as the absolute coordinates of th e solid line and the bro ken line, are the basic components of the high high-precision map. At present, point cloud data are still one of the most important data source of the high high-precision map. So, how to get road markings information from original point clouds automatically deserve study. In this paper, point cloud is sliced by the mileage of the road, then each slice is projected onto respective vertical section section. Random Sample Consensus (RANSAC) algorithm is applied to establish road surface buffer area . Finally, moving window filtering is used to extract road surface point cloud from road surface buffer area area. On this basis, the road surface point cloud image is transformed into raster image with a certain resolution by using the method of inverse distance weighted interpolation , and the grid image is converted into binary image by using the method of adaptive threshold segmentation based on the integral graph. Then the method of the Euclidean clustering is used to extract the road markings point cloud from the binary image. Characteristic attribute detection is applied to recognize solid line marking from all clusters. Deep learning network framework pointnet++ is applied to recognize remain road markings including guideline, broken line, straight arrow, and right turn arrow.

scholarly journals A DEEP LEARNING FRAMEWORK FOR ROADS NETWORK DAMAGE ASSESSMENT USING POST-EARTHQUAKE LIDAR DATA

2019 ◽  
Vol XLII-4/W18 ◽  
pp. 955-961 ◽  
Author(s):  
S. T. Seydi ◽  
H. Rastiveis
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Damage Assessment ◽  
Point Cloud ◽  
High Performance ◽  
Damage Index ◽  
Lidar Data ◽  
The Road ◽  
The Status ◽  
Road Segments

Abstract. Roads network are the most important parts of urban infrastructures, which can cause difficulty to the city whenever they undergo a problem. This paper aims to provide and implement a deep learning-based method to determine the status of the streets network after an earthquake using LiDAR point cloud. The proposed framework composes of three main phases: (1) Deep features of LiDAR data are extracted using a Convolutional Neural Network (CNN). (2) The extracted features are used in a multilayer perceptron (MLP) neural network in which debris areas inside the road network are detected. (3) The amount of debris in each road is applied to damage index for classifying the road segments into blocked or un-blocked. To evaluate the efficiency of the proposed framework, LiDAR point cloud of the Port-au-Prince, Haiti after the 2010 Haiti earthquake was used. The overall accuracy of more than 97% proved the high performance of this framework for debris detection. Moreover, analyzing damage assessment of 37 road segments based on the detected debris and comparing to a visually generated damaged map, 31 of the road segments were correctly labelled as either blocked or un-blocked.

scholarly journals Spherically Stratified Point Projection: Feature Image Generation for Object Classification Using 3D LiDAR Data

Sensors ◽  
2021 ◽  
Vol 21 (23) ◽  
pp. 7860
Author(s):  
Chulhee Bae ◽  
Yu-Cheol Lee ◽  
Wonpil Yu ◽  
Sejin Lee
Keyword(s):  
Three Dimensional ◽  
Object Classification ◽  
Point Clouds ◽  
Lidar Data ◽  
Image Generation ◽  
Channel Integration ◽  
Occupancy Probability ◽  
Evidence Collection ◽  
Data Points ◽  
Point Projection

Three-dimensional point clouds have been utilized and studied for the classification of objects at the environmental level. While most existing studies, such as those in the field of computer vision, have detected object type from the perspective of sensors, this study developed a specialized strategy for object classification using LiDAR data points on the surface of the object. We propose a method for generating a spherically stratified point projection (sP2) feature image that can be applied to existing image-classification networks by performing pointwise classification based on a 3D point cloud using only LiDAR sensors data. The sP2’s main engine performs image generation through spherical stratification, evidence collection, and channel integration. Spherical stratification categorizes neighboring points into three layers according to distance ranges. Evidence collection calculates the occupancy probability based on Bayes’ rule to project 3D points onto a two-dimensional surface corresponding to each stratified layer. Channel integration generates sP2 RGB images with three evidence values representing short, medium, and long distances. Finally, the sP2 images are used as a trainable source for classifying the points into predefined semantic labels. Experimental results indicated the effectiveness of the proposed sP2 in classifying feature images generated using the LeNet architecture.

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