scholarly journals INDOOR MOBILE MAPPING SYSTEM AND CROWD SIMULATION TO SUPPORT SCHOOL REOPENING BECAUSE OF COVID-19: A CASE STUDY

2020 ◽  
Vol XLIV-3/W1-2020 ◽  
pp. 29-36
Author(s):  
S. Comai ◽  
S. Costa ◽  
S. Mastrolembo Ventura ◽  
G. Vassena ◽  
L. C. Tagliabue ◽  
...  
Keyword(s):  
Test Site ◽  
End Users ◽  
Crowd Simulation ◽  
Simulation Software ◽  
Mobile Mapping ◽  
Social Distancing ◽  
Mapping System ◽  
The Social ◽  
Mobile Mapping System

Abstract. Occupancy analyses represent a crucial topic for building performance. At present, this is even true because of the pandemic emergency due to SARS-CoV-2 and the need to support the functional analysis of building spaces in relation to social distancing rules. Moreover, the need to assess the suitability of spaces in high occupancy buildings as the educational ones, for which occupancy evaluations result pivotal to ensure the safety of the end-users in their daily activities, is a priority. The proposed paper investigates the steps that are needed to secure a safe re-opening of an educational building. A case study has been selected as a test site to analyse the re-opening steps as required by Italian protocols and regulations. This analysis supported the school director of a 2-to-10 year old school and its team in the decision-making process that led to the safe school re-opening. Available plants and elevations of the building were collected and a fast digital survey was carried out using the mobile laser scanner technology (iMMS - Indoor Mobile Mapping System) in order to acquire three-dimensional geometries and digital photographic documentation of the spaces. A crowd simulation software (i.e. Oasys MassMotion) was implemented to analyse end-users flows; the social distance parameter was set in its proximity modelling tools in order to check the compliance of spaces and circulation paths to the social distancing protocols. Contextually to the analysis of users flows, a plan of hourly air changes to maintain a high quality of the environments has been defined.

scholarly journals BACKPACK MOBILE MAPPING SOLUTION FOR DTM EXTRACTION OF LARGE INACCESSIBLE SPACES

2019 ◽  
Vol XLII-2/W15 ◽  
pp. 473-480
Author(s):  
F. Fassi ◽  
L. Perfetti
Keyword(s):  
Digital Terrain Model ◽  
Small Scale ◽  
Mobile Mapping ◽  
Terrain Model ◽  
Digital Terrain ◽  
Mapping System ◽  
Mobile Mapping System ◽  
The One ◽  
Mapping Solution

<p><strong>Abstract.</strong> The paper presents the case study of the complete 3D survey of the area of the Fort of Pietole in Borgo Virgilio using the Leica Pegasus Backpack wearable Mobile Mapping System (MMS). Surveying the site is challenging because of its complex topology on the one hand (with notably narrow passages) and because of the presence of vegetation on the other. The framework within which this research takes place is the Fort of Pietole survey project that aims at the extraction of the Digital Terrain Model (DTM) of the area and the georeferencing of the fort defensive structures. The requirement of the project is the 3D reconstruction of the whole area at an accuracy that stands between a big scale environmental survey and a small-scale architectonic survey (1&amp;thinsp;:&amp;thinsp;500).</p> <p>The project is the opportunity to discuss the state of the art of wearable MMS, and to test the versatility and accuracy outcomes of the Pegasus Backpack under varying and challenging condition (indoor-outdoor, even-uneven pavement, satellite covered-denied areas) with the ambitious goal to use only the backpack MMS to record all the data from the DTM to the indoor narrow structures.</p>

scholarly journals INVESTIGATING THE PERFORMANCE OF A HANDHELD MOBILE MAPPING SYSTEM IN DIFFERENT OUTDOOR SCENARIOS

2021 ◽  
Vol XLIII-B1-2021 ◽  
pp. 103-109
Author(s):  
E. Maset ◽  
S. Cucchiaro ◽  
F. Cazorzi ◽  
F. Crosilla ◽  
A. Fusiello ◽  
...  
Keyword(s):  
Laser Scanning ◽  
Point Clouds ◽  
Qualitative Assessment ◽  
Case Scenario ◽  
Mobile Mapping ◽  
Mapping System ◽  
Mobile Mapping System ◽  
Survey Instruments ◽  
Large Building

Abstract. In recent years, portable Mobile Mapping Systems (MMSs) are emerging as valuable survey instruments for fast and efficient mapping of both internal and external environments. The aim of this work is to assess the performance of a commercial handheld MMS, Gexcel HERON Lite, in two different outdoor applications. The first is the mapping of a large building, which represents a standard use-case scenario of this technology. Through the second case study, that consists in the survey of a torrent reach, we investigate instead the applicability of the handheld MMS for natural environment monitoring, a field in which portable systems are not yet widely employed. Quantitative and qualitative assessment is presented, comparing the point clouds obtained from the HERON Lite system against reference models provided by traditional techniques (i.e., Terrestrial Laser Scanning and Photogrammetry).

scholarly journals ROBUST AND ACCURATE IMAGE-BASED GEOREFERENCING EXPLOITING RELATIVE ORIENTATION CONSTRAINTS

2018 ◽  
Vol IV-2 ◽  
pp. 57-64
Author(s):  
S. Cavegn ◽  
S. Blaser ◽  
S. Nebiker ◽  
N. Haala
Keyword(s):  
Test Site ◽  
Urban Environments ◽  
Relative Orientation ◽  
Bundle Adjustment ◽  
Test Field ◽  
Mobile Mapping ◽  
Mapping System ◽  
Mobile Mapping System ◽  
Accuracy Increase ◽  
Orientation Parameters

Urban environments with extended areas of poor GNSS coverage as well as indoor spaces that often rely on real-time SLAM algorithms for camera pose estimation require sophisticated georeferencing in order to fulfill our high requirements of a few centimeters for absolute 3D point measurement accuracies. Since we focus on image-based mobile mapping, we extended the structure-from-motion pipeline COLMAP with georeferencing capabilities by integrating exterior orientation parameters from direct sensor orientation or SLAM as well as ground control points into bundle adjustment. Furthermore, we exploit constraints for relative orientation parameters among all cameras in bundle adjustment, which leads to a significant robustness and accuracy increase especially by incorporating highly redundant multi-view image sequences. We evaluated our integrated georeferencing approach on two data sets, one captured outdoors by a vehicle-based multi-stereo mobile mapping system and the other captured indoors by a portable panoramic mobile mapping system. We obtained mean RMSE values for check point residuals between image-based georeferencing and tachymetry of 2&amp;thinsp;cm in an indoor area, and 3&amp;thinsp;cm in an urban environment where the measurement distances are a multiple compared to indoors. Moreover, in comparison to a solely image-based procedure, our integrated georeferencing approach showed a consistent accuracy increase by a factor of 2&amp;ndash;3 at our outdoor test site. Due to pre-calibrated relative orientation parameters, images of all camera heads were oriented correctly in our challenging indoor environment. By performing self-calibration of relative orientation parameters among respective cameras of our vehicle-based mobile mapping system, remaining inaccuracies from suboptimal test field calibration were successfully compensated.

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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