scholarly journals Evaluation of Long-Range Mobile Mapping System (MMS) and Close-Range Photogrammetry for Deformation Monitoring. A Case Study of Cortes de Pallás in Valencia (Spain)

2020 ◽  
Vol 10 (19) ◽  
pp. 6831
Author(s):  
Francesco Di Stefano ◽  
Miriam Cabrelles ◽  
Luis García-Asenjo ◽  
José Luis Lerma ◽  
Eva Savina Malinverni ◽  
...  
Keyword(s):  
Long Range ◽  
New Technologies ◽  
3D Models ◽  
Deformation Monitoring ◽  
Mobile Mapping ◽  
Close Range Photogrammetry ◽  
Mapping System ◽  
Mobile Mapping System ◽  
Close Range ◽  
Landslide Deformation

This contribution describes the methodology applied to evaluate the suitability of a Long-Range Mobile Mapping System to be integrated with other techniques that are currently used in a large and complex landslide deformation monitoring project carried out in Cortes de Pallás, in Valencia (Spain). Periodical geodetic surveys provide a reference frame realized by 10 pillars and 15 additional check points placed in specific points of interest, all with millimetric accuracy. The combined use of Close-Range Photogrammetry provides a well-controlled 3D model with 1–3 cm accuracy, making the area ideal for testing new technologies. Since some zones of interest are usually obstructed by construction, trees, or lamp posts, a possible solution might be the supplementary use of dynamic scanning instruments with the mobile mapping solution Kaarta Stencil 2 to collect the missing data. However, the reliability of this technology has to be assessed and validated before being integrated into the existing 3D models in the well-controlled area of Cortes de Pallás. The results of the experiment show that the accuracy achieved are compatible with those obtained from Close-Range Photogrammetry and can also be safely used to supplement image-based information for monitoring with 3–8 cm overall accuracy.

scholarly journals EXTRACTION AND SHAPE RECONSTRUCTION OF GUARDRAILS USING MOBILE MAPPING DATA

2019 ◽  
Vol XLII-2/W13 ◽  
pp. 1061-1068
Author(s):  
H. Matsumoto ◽  
Y. Mori ◽  
H. Masuda
Keyword(s):  
Urban Areas ◽  
Point Clouds ◽  
3D Models ◽  
Shape Reconstruction ◽  
Mobile Mapping ◽  
Mapping System ◽  
Dense Point ◽  
Mobile Mapping System ◽  
3D Shapes ◽  
Method Point

<p><strong>Abstract.</strong> The mobile mapping system (MMS) can acquire dense point-clouds of roads and roadside features. Roads are often separated into roadways and walkways in many urban areas. Since guardrails are installed to separate roadways and sidewalks, it is important to detect guardrails from point-clouds and reconstruct their 3D models for 3D street maps. Since there are a large variety of designs for guardrails in Japan, flexible methods are required for detection and reconstruction of guardrails. In this paper, we propose a new method for extracting guardrails from point-clouds, and reconstructing their 3D models. Since the MMS captures point-clouds and camera images synchronously, guardrails are detected using both point-clouds and images. In our method, point-clouds are segmented into small segments, and corresponding images are cropped from camera images. Then cropped images are classified into two classes of guardrails and others using the convolutional neural network. When guardrail points are obtained, 3D models of guardrails are reconstructed. However, point-clouds of guardrails are too sparse to reconstruct 3D shapes when guardrails consist of thin pipes. Since the same unit shape repeatedly appears in a guardrail, we create dense point-clouds by superimposing points of unit shapes. Then we reconstruct 3D shapes of pipes, beams, and poles of guardrails. In our evaluation using point-clouds in urban areas, our method could achieve good results of extraction and shape reconstruction of guardrails.</p>

scholarly journals Photogrammetric Applications of Immersive Video Cameras

2014 ◽  
Vol II-5 ◽  
pp. 211-218 ◽  
Author(s):  
K. Kwiatek ◽  
R. Tokarczyk
Keyword(s):  
Low Cost ◽  
Base Line ◽  
Mobile Mapping ◽  
Heritage Sites ◽  
3D Objects ◽  
Mapping System ◽  
Short Period ◽  
Mobile Mapping System ◽  
Close Range ◽  
Live Action

The paper investigates immersive videography and its application in close-range photogrammetry. Immersive video involves the capture of a live-action scene that presents a 360° field of view. It is recorded simultaneously by multiple cameras or microlenses, where the principal point of each camera is offset from the rotating axis of the device. This issue causes problems when stitching together individual frames of video separated from particular cameras, however there are ways to overcome it and applying immersive cameras in photogrammetry provides a new potential. The paper presents two applications of immersive video in photogrammetry. At first, the creation of a low-cost mobile mapping system based on Ladybug®3 and GPS device is discussed. The amount of panoramas is much too high for photogrammetric purposes as the base line between spherical panoramas is around 1 metre. More than 92 000 panoramas were recorded in one Polish region of Czarny Dunajec and the measurements from panoramas enable the user to measure the area of outdoors (adverting structures) and billboards. A new law is being created in order to limit the number of illegal advertising structures in the Polish landscape and immersive video recorded in a short period of time is a candidate for economical and flexible measurements off-site. The second approach is a generation of 3d video-based reconstructions of heritage sites based on immersive video (structure from immersive video). A mobile camera mounted on a tripod dolly was used to record the interior scene and immersive video, separated into thousands of still panoramas, was converted from video into 3d objects using Agisoft Photoscan Professional. The findings from these experiments demonstrated that immersive photogrammetry seems to be a flexible and prompt method of 3d modelling and provides promising features for mobile mapping systems.

scholarly journals Development of a Mobile Mapping System for Multi-Purpose Applications Composed of a Low-Cost Inertial Measuring Unit, a GNSS-Receiver and a Close-Range LIDAR

Proceedings ◽  
2019 ◽  
Vol 19 (1) ◽  
pp. 9
Author(s):  
Pardo ◽  
Abadía ◽  
Sternberg
Keyword(s):  
Low Cost ◽  
Laser Scanner ◽  
Mobile Mapping ◽  
Gnss Receiver ◽  
Sensor Platform ◽  
Mapping System ◽  
Inertial Measuring Unit ◽  
Mobile Mapping System ◽  
Close Range ◽  
Measuring Unit

The research project “Development of a Mobile Mapping System for multi-purpose applications composed of a low-cost inertial measuring unit, a GNSS receiver and a close-range LIDAR” consists on considerations about the design of an aerial and a sensor platform, which can also be used separately. The aim of the project is the development of a measurement platform, which performs a direct scan of the Earth’s surface by means of measurements with a laser scanner supported by several sensors to determine their position. The geo-referencing of the data will initially take place in post-processing.

scholarly journals Deep Learning Based Pavement Inspection Using Self-Reconfigurable Robot

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2595
Author(s):  
Balakrishnan Ramalingam ◽  
Abdullah Aamir Hayat ◽  
Mohan Rajesh Elara ◽  
Braulio Félix Gómez ◽  
Lim Yi ◽  
...  
Keyword(s):  
Deep Learning ◽  
Semantic Segmentation ◽  
High Accuracy ◽  
Experimental Results ◽  
Mobile Mapping ◽  
Mapping System ◽  
Mobile Mapping System ◽  
Reconfigurable Robot ◽  
Nvidia Gpu ◽  
Inspection Task

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.

A Mobile Mapping System for road data capture based on 3D road model

2006 ◽  
Author(s):  
Kiichiro Ishikawa ◽  
Jun-ichi Takiguchi ◽  
Yoshiharu Amano ◽  
Takumi Hashizume
Keyword(s):  
Data Capture ◽  
Mobile Mapping ◽  
Mapping System ◽  
Mobile Mapping System ◽  
Road Data
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