Design and implementation of Urban Road Surface Facilities Management System based on mobile mapping system

Author(s):  
Yang Liu ◽  
Mingyi Du
ETRI Journal ◽  
2006 ◽  
Vol 28 (3) ◽  
pp. 265-274 ◽  
Author(s):  
Seung-Yong Lee ◽  
Kyoung-Ho Choi ◽  
In-Hak Joo ◽  
Seong-Ik Cho ◽  
Jong-Hyun Park

2020 ◽  
Vol 14 (1) ◽  
pp. 39-54 ◽  
Author(s):  
Erik Heinz ◽  
Christian Eling ◽  
Lasse Klingbeil ◽  
Heiner Kuhlmann

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.


Author(s):  
D. Yagishita ◽  
H. Chikatsu

In recent years, high precision and high resolution road surface orthophotos have been generated using video cameras mounted on surveying vehicles. However, there is a serious issue in generating an orthophoto from this image. The shadows of the surrounding structures and vehicles on the road surface cause a lack of information and decrease in visibility. Therefore, the shadows should be removed from the images for exact road management. On the other hand, the Mobile Mapping System with a laser scanner mounted on vehicles has been receiving more attention because the laser scanner intensity is almost unaffected by shadows. This paper presents shadow extraction and shadow correction for generating road surface orthophotos using the laser scanner intensity.


2009 ◽  
Vol 2009 (0) ◽  
pp. _1A2-B22_1-_1A2-B22_2
Author(s):  
Kiichiro ISHIKAWA ◽  
Masashi TAKANO ◽  
Yoshihiro SHIMA ◽  
Jun-ichi TAKIGUCHI ◽  
Yoshiharu AMANO ◽  
...  

2008 ◽  
Vol 2008 (0) ◽  
pp. _1A1-G21_1-_1A1-G21_3
Author(s):  
Shuhei ONO ◽  
Kiichiro ISHIKAWA ◽  
Jun-ichi TAKIGUCHI ◽  
Yoshihiro SHIMA ◽  
Yoshiharu AMANO ◽  
...  

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2595
Author(s):  
Balakrishnan Ramalingam ◽  
Abdullah Aamir Hayat ◽  
Mohan Rajesh Elara ◽  
Braulio Félix Gómez ◽  
Lim Yi ◽  
...  

The pavement inspection task, which mainly includes crack and garbage detection, is essential and carried out frequently. The human-based or dedicated system approach for inspection can be easily carried out by integrating with the pavement sweeping machines. This work proposes a deep learning-based pavement inspection framework for self-reconfigurable robot named Panthera. Semantic segmentation framework SegNet was adopted to segment the pavement region from other objects. Deep Convolutional Neural Network (DCNN) based object detection is used to detect and localize pavement defects and garbage. Furthermore, Mobile Mapping System (MMS) was adopted for the geotagging of the defects. The proposed system was implemented and tested with the Panthera robot having NVIDIA GPU cards. The experimental results showed that the proposed technique identifies the pavement defects and litters or garbage detection with high accuracy. The experimental results on the crack and garbage detection are presented. It is found that the proposed technique is suitable for deployment in real-time for garbage detection and, eventually, sweeping or cleaning tasks.


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