Pothole Mapping and Patching Quantity Estimates using LiDAR-Based Mobile Mapping Systems

2020 ◽  
Vol 2674 (9) ◽  
pp. 124-134
Author(s):  
Radhika Ravi ◽  
Ayman Habib ◽  
Darcy Bullock
Keyword(s):  
Point Clouds ◽  
Relative Accuracy ◽  
Mobile Mapping ◽  
Cloud Data ◽  
Pavement Distress ◽  
The Road ◽  
Mapping System ◽  
Mobile Mapping System ◽  
The Cost ◽  
Mapping Systems

Pavement distress or pothole mapping is important to public agencies responsible for maintaining roadways. The efficient capture of 3D point cloud data using mapping systems equipped with LiDAR eliminates the time-consuming and labor-intensive manual classification and quantity estimates. This paper proposes a methodology to map potholes along the road surface using ultra-high accuracy LiDAR units onboard a wheel-based mobile mapping system. LiDAR point clouds are processed to detect and report the location and severity of potholes by identifying the below-road 3D points pertaining to potholes, along with their depths. The surface area and volume of each detected pothole is also estimated along with the volume of its minimum bounding box to serve as an aide to choose the ideal method of repair as well as to estimate the cost of repair. The proposed approach was tested on a 10 mi-long segment on a U.S. Highway and it is observed to accurately detect potholes with varying severity and different causes. A sample of potholes detected in a 1 mi segment has been reported in the experimental results of this paper. The point clouds generated using the system are observed to have a single-track relative accuracy of less than ±1 cm and a multi-track relative accuracy of ±1–2 cm, which has been verified through comparing point clouds captured by different sensors from different tracks.

On the applicability of a scan-based mobile mapping system for monitoring the planarity and subsidence of road surfaces – Pilot study on the A44n motorway in Germany

2020 ◽  
Vol 14 (1) ◽  
pp. 39-54 ◽  
Author(s):  
Erik Heinz ◽  
Christian Eling ◽  
Lasse Klingbeil ◽  
Heiner Kuhlmann
Keyword(s):  
Standard Deviation ◽  
Point Clouds ◽  
Road Surface ◽  
Control Points ◽  
Mobile Mapping ◽  
The Road ◽  
Mapping System ◽  
Road Surfaces ◽  
Mobile Mapping System ◽  
Physical Height

AbstractKinematic laser scanning is widely used for the fast and accurate acquisition of road corridors. In this context, road monitoring is a crucial application, since deficiencies of the road surface due to non-planarity and subsidence put traffic at risk. In recent years, a Mobile Mapping System (MMS) has been developed at the University of Bonn, consisting of a GNSS/IMU unit and a 2D laser scanner. The goal of this paper is to evaluate the accuracy and precision of this MMS, where the height component is of main interest. Following this, the applicability of the MMS for monitoring the planarity and subsidence of road surfaces is analyzed. The test area for this study is a 6 km long section of the A44n motorway in Germany. For the evaluation of the MMS, leveled control points along the motorway as well as point cloud comparisons of repeated passes were used. In order to transform the ellipsoidal heights of the MMS into the physical height system of the control points, undulations were utilized. In this respect, a local tilt correction for the geoid model was determined based on GNSS baselines and leveling, leading to a physical height accuracy of the MMS of < 10 mm (RMS). The related height precision has a standard deviation of about 5 mm. Hence, a potential subsidence of the road surface in the order of a few cm is detectable. In addition, the point clouds were used to analyze the planarity of the road surface. In the course of this, the cross fall of the road was estimated with a standard deviation of < 0.07 %. Yet, no deficiencies of the road surface in the form of significant rut depths or fictive water depths were detected, indicating the proper condition of the A44n motorway. According to our tests, the MMS is appropriate for road monitoring.

Curb Detection and Accessibility Evaluation from Low-Density Mobile Mapping Point Cloud Data

2018 ◽  
Vol 12 (3) ◽  
pp. 376-385
Author(s):  
Kiichiro Ishikawa ◽  
Daisuke Kubo ◽  
Yoshiharu Amano ◽  
◽  
Keyword(s):  
Point Clouds ◽  
Noise Removal ◽  
Low Density ◽  
Mobile Mapping ◽  
Cloud Data ◽  
Mapping System ◽  
Mobile Mapping System ◽  
System Data ◽  
Scan Data

Our goal is to automatically classify objects from Mobile Mapping System data to enable the automatic construction of dynamic maps. We aimed at the extraction of curbstones and classification of curb types. Although there is much research about curbstones being recognized from laser-scanned point clouds, there are few methods to classify curb types. In this paper, we propose a method to extract curbstones from low-density-type laser scan data. We also propose a method to distinguish whether curbstones allow access to off-road facilities. Evaluation tests give anF-measure of ≥94.4% and an accessibility classification accuracy of ≥99.6%. Moreover, the results of applying multiple filters to noise removal are compared.

scholarly journals MONITORING CAPABILITIES OF A MOBILE MAPPING SYSTEM BASED ON NAVIGATION QUALITIES

2016 ◽  
Vol XLI-B1 ◽  
pp. 625-631 ◽  
Author(s):  
H. Jing ◽  
N. Slatcher ◽  
X. Meng ◽  
G. Hunter
Keyword(s):  
Change Detection ◽  
Point Clouds ◽  
Mobile Mapping ◽  
Mapping System ◽  
Short Period ◽  
Laser Scanners ◽  
Mobile Mapping System ◽  
Mobile Mapping Systems ◽  
Mapping Systems

Mobile mapping systems are becoming increasingly popular as they can build 3D models of the environment rapidly by using a laser scanner that is integrated with a navigation system. 3D mobile mapping has been widely used for applications such as 3D city modelling and mapping of the scanned environments. However, accurate mapping relies on not only the scanner’s performance but also on the quality of the navigation results (accuracy and robustness) . This paper discusses the potentials of using 3D mobile mapping systems for landscape change detection, that is traditionally carried out by terrestrial laser scanners that can be accurately geo-referenced at a static location to produce highly accurate dense point clouds. Yet compared to conventional surveying using terrestrial laser scanners, several advantages of mobile mapping systems can be identified. A large area can be monitored in a relatively short period, which enables high repeat frequency monitoring without having to set-up dedicated stations. However, current mobile mapping applications are limited by the quality of navigation results, especially in different environments. The change detection ability of mobile mapping systems is therefore significantly affected by the quality of the navigation results. This paper presents some data collected for the purpose of monitoring from a mobile platform. The datasets are analysed to address current potentials and difficulties. The change detection results are also presented based on the collected dataset. Results indicate the potentials of change detection using a mobile mapping system and suggestions to enhance quality and robustness.

scholarly journals Conditional Adversarial Networks for Multimodal Photo-Realistic Point Cloud Rendering

2020 ◽  
Vol 88 (3-4) ◽  
pp. 257-269 ◽  
Author(s):  
Torben Peters ◽  
Claus Brenner
Keyword(s):  
Point Cloud ◽  
Point Clouds ◽  
Generative Adversarial Networks ◽  
Mobile Mapping ◽  
Cloud Data ◽  
Adversarial Networks ◽  
Mapping System ◽  
Different Seasons ◽  
Mobile Mapping System ◽  
Image Pairs

Abstract We investigate whether conditional generative adversarial networks (C-GANs) are suitable for point cloud rendering. For this purpose, we created a dataset containing approximately 150,000 renderings of point cloud–image pairs. The dataset was recorded using our mobile mapping system, with capture dates that spread across 1 year. Our model learns how to predict realistically looking images from just point cloud data. We show that we can use this approach to colourize point clouds without the usage of any camera images. Additionally, we show that by parameterizing the recording date, we are even able to predict realistically looking views for different seasons, from identical input point clouds.

scholarly journals OBJECT DETECTION FROM MMS IMAGERY USING DEEP LEARNING FOR GENERATION OF ROAD ORTHOPHOTOS

2018 ◽  
Vol XLII-2 ◽  
pp. 573-577
Author(s):  
Y. Li ◽  
M. Sakamoto ◽  
T. Shinohara ◽  
T. Satoh
Keyword(s):  
Point Cloud ◽  
Point Cloud Data ◽  
Mobile Mapping ◽  
Cloud Data ◽  
The Road ◽  
Detection Model ◽  
Proposed Model ◽  
Mapping System ◽  
Mobile Mapping System

In recent years, extensive research has been conducted to automatically generate high-accuracy and high-precision road orthophotos using images and laser point cloud data acquired from a mobile mapping system (MMS). However, it is necessary to mask out non-road objects such as vehicles, bicycles, pedestrians and their shadows in MMS images in order to eliminate erroneous textures from the road orthophoto. Hence, we proposed a novel vehicle and its shadow detection model based on Faster R-CNN for automatically and accurately detecting the regions of vehicles and their shadows from MMS images. The experimental results show that the maximum recall of the proposed model was high &amp;ndash; 0.963 (intersection-over-union &amp;gt;&amp;thinsp;0.7) &amp;ndash; and the model could identify the regions of vehicles and their shadows accurately and robustly from MMS images, even when they contain varied vehicles, different shadow directions, and partial occlusions. Furthermore, it was confirmed that the quality of road orthophoto generated using vehicle and its shadow masks was significantly improved as compared to those generated using no masks or using vehicle masks only.

scholarly journals MONITORING CAPABILITIES OF A MOBILE MAPPING SYSTEM BASED ON NAVIGATION QUALITIES

2016 ◽  
Vol XLI-B1 ◽  
pp. 625-631
Author(s):  
H. Jing ◽  
N. Slatcher ◽  
X. Meng ◽  
G. Hunter
Keyword(s):  
Change Detection ◽  
Point Clouds ◽  
Mobile Mapping ◽  
Mapping System ◽  
Short Period ◽  
Laser Scanners ◽  
Mobile Mapping System ◽  
Mobile Mapping Systems ◽  
Mapping Systems

Mobile mapping systems are becoming increasingly popular as they can build 3D models of the environment rapidly by using a laser scanner that is integrated with a navigation system. 3D mobile mapping has been widely used for applications such as 3D city modelling and mapping of the scanned environments. However, accurate mapping relies on not only the scanner’s performance but also on the quality of the navigation results (accuracy and robustness) . This paper discusses the potentials of using 3D mobile mapping systems for landscape change detection, that is traditionally carried out by terrestrial laser scanners that can be accurately geo-referenced at a static location to produce highly accurate dense point clouds. Yet compared to conventional surveying using terrestrial laser scanners, several advantages of mobile mapping systems can be identified. A large area can be monitored in a relatively short period, which enables high repeat frequency monitoring without having to set-up dedicated stations. However, current mobile mapping applications are limited by the quality of navigation results, especially in different environments. The change detection ability of mobile mapping systems is therefore significantly affected by the quality of the navigation results. This paper presents some data collected for the purpose of monitoring from a mobile platform. The datasets are analysed to address current potentials and difficulties. The change detection results are also presented based on the collected dataset. Results indicate the potentials of change detection using a mobile mapping system and suggestions to enhance quality and robustness.

scholarly journals Performance Assessment of Mobile Laser Scanning Systems Using Velodyne Hdl-32e

2021 ◽  
Vol 1 (1) ◽  
pp. 28-33
Author(s):  
Bashar Alsadik
Keyword(s):  
Laser Scanning ◽  
Impact Analysis ◽  
Orientation Angle ◽  
Point Clouds ◽  
Mobile Mapping ◽  
Ongoing Research ◽  
Mapping System ◽  
Mobile Mapping System ◽  
Mobile Mapping Systems ◽  
Mapping Systems

Mapping systems using multi-beam LiDARs are widely used nowadays for different geospatial applications graduating from indoor projects to outdoor city-wide projects. These mobile mapping systems can be either ground-based or aerial-based systems and are mostly equipped with inertial navigation systems INS. The Velodyne HDL-32 LiDAR is a well-known 360° spinning multi-beam laser scanner that is widely used in outdoor and indoor mobile mapping systems. The performance of such LiDARs is an ongoing research topic which is quite important for the quality assurance and quality control topic. The performance of this LiDAR type is correlated to many factors either related to the device itself or the design of the mobile mapping system. Regarding design, most of the mapping systems are equipped with a single Velodyne HDL32 in a specific orientation angle which is different among the mapping systems manufacturers. The LiDAR orientation angle has a significant impact on the performance in terms of the density and coverage of the produced point clouds. Furthermore, during the lifetime of this multi-beam LiDAR, one or more beams may be defected and then either continue the production or returned to the manufacturer to be fixed which then cost time and money. In this paper, the design impact analysis of a mobile laser scanning (MLS) system equipped with a single Velodyne HDL-32E will be clarified and a clear relationship is given between the orientation angle of the LiDAR and the output density of points. The ideal angular orientation of a single Velodyne HDL-32E is found to be at 35° in a mobile mapping system. Furthermore, we investigated the degradation of points density when one of the 32 beams is defected and quantified the density loss percentage and to the best of our knowledge, this is not presented in literature before. It is found that a maximum of about 8% point density loss occurs on the ground and 4% on the facades when having a defected beam of the Velodyne HDL-32E.   

scholarly journals IQPC 2015 TRACK: TREE SEPARATION AND CLASSIFICATION IN MOBILE MAPPING LIDAR DATA

2015 ◽  
Vol XL-3/W3 ◽  
pp. 607-612 ◽  
Author(s):  
B. Gorte ◽  
S. Oude Elberink ◽  
B. Sirmacek ◽  
J. Wang
Keyword(s):  
Point Clouds ◽  
Mobile Mapping ◽  
The Road ◽  
Mapping System ◽  
Mobile Mapping System ◽  
Street Furniture ◽  
Set Up ◽  
Novel Algorithms ◽  
Individual Trees ◽  
System Point

The European FP7 project IQmulus yearly organizes several processing contests, where submissions are requested for novel algorithms for point cloud and other <i>big</i> geodata processing. This paper describes the set-up and execution of a contest having the purpose to evaluate state-of-the-art algorithms for Mobile Mapping System point clouds, in order to detect and identify (individual) trees. By the nature of MMS these are trees in the vicinity of the road network (rather than in forests). Therefore, part of the challenge is distinguishing between trees and other objects, such as buildings, street furniture, cars etc. Three submitted segmentation and classification algorithms are thus evaluated.

scholarly journals APPLICATION OF MLS DATA TO THE ASSESSMENT OF SAFETY-RELATED FEATURES IN THE SURROUNDING AREA OF AUTOMATICALLY DETECTED PEDESTRIAN CROSSINGS

2018 ◽  
Vol XLII-2 ◽  
pp. 1067-1074 ◽  
Author(s):  
M. Soilán ◽  
B. Riveiro ◽  
A. Sánchez-Rodríguez ◽  
L. M. González-deSantos
Keyword(s):  
Laser Scanning ◽  
Geographical Information ◽  
Mobile Mapping ◽  
Cloud Data ◽  
Traffic Lights ◽  
The Road ◽  
Processing Module ◽  
Pedestrian Crossing ◽  
Mobile Mapping Systems ◽  
Mapping Systems

During the last few years, there has been a huge methodological development regarding the automatic processing of 3D point cloud data acquired by both terrestrial and aerial mobile mapping systems, motivated by the improvement of surveying technologies and hardware performance. This paper presents a methodology that, in a first place, extracts geometric and semantic information regarding the road markings within the surveyed area from Mobile Laser Scanning (MLS) data, and then employs it to isolate street areas where pedestrian crossings are found and, therefore, pedestrians are more likely to cross the road. Then, different safety-related features can be extracted in order to offer information about the adequacy of the pedestrian crossing regarding its safety, which can be displayed in a Geographical Information System (GIS) layer. These features are defined in four different processing modules: Accessibility analysis, traffic lights classification, traffic signs classification, and visibility analysis. The validation of the proposed methodology has been carried out in two different cities in the northwest of Spain, obtaining both quantitative and qualitative results for pedestrian crossing classification and for each processing module of the safety assessment on pedestrian crossing environments.

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