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 cm in an indoor area, and 3 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 2–3 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.

Automatic Registration of Mobile Mapping System Lidar Points and Panoramic-Image Sequences by Relative Orientation Model

2021 ◽  
Vol 87 (12) ◽  
pp. 913-922
Author(s):  
Ningning Zhu ◽  
Bisheng Yang ◽  
Zhen Dong ◽  
Chi Chen ◽  
Xia Huang ◽  
...  
Keyword(s):  
Image Sequence ◽  
Image Sequences ◽  
Relative Orientation ◽  
Panoramic Image ◽  
Feature Points ◽  
Mobile Mapping ◽  
Orientation Model ◽  
Mapping System ◽  
Panoramic Images ◽  
Mobile Mapping System

To register mobile mapping system (MMS) lidar points and panoramic-image sequences, a relative orientation model of panoramic images (PROM) is proposed. The PROM is suitable for cases in which attitude or orientation parameters are unknown in the panoramic-image sequence. First, feature points are extracted and matched from panoramic-image pairs using the SURF algorithm. Second, these matched feature points are used to solve the relative attitude parameters in the PROM. Then, combining the PROM with the absolute position and attitude parameters of the initial panoramic image, the MMS lidar points and panoramic-image sequence are registered. Finally, the registration accuracy of the PROM method is assessed using corresponding points manually selected from the MMSlidar points and panoramic-image sequence. The results show that three types of MMSdata sources are registered accurately based on the proposed registration method. Our method transforms the registration of panoramic images and lidar points into image feature-point matching, which is suitable for diverse road scenes compared with existing methods.

scholarly journals HYBRID MODELS FOR TRAJECTORY ERROR MODELLING IN URBAN ENVIRONMENTS

2016 ◽  
Vol XLI-B3 ◽  
pp. 805-811
Author(s):  
E. Angelatsa ◽  
M. E. Parés ◽  
I. Colomina
Keyword(s):  
Error Function ◽  
Real Data ◽  
Continuous Functions ◽  
Error Component ◽  
Urban Environments ◽  
Mobile Mapping ◽  
Trajectory Error ◽  
Mapping System ◽  
Wide Range ◽  
Mobile Mapping System

This paper tackles the first step of any strategy aiming to improve the trajectory of terrestrial mobile mapping systems in urban environments. We present an approach to model the error of terrestrial mobile mapping trajectories, combining deterministic and stochastic models. Due to urban specific environment, the deterministic component will be modelled with non-continuous functions composed by linear shifts, drifts or polynomial functions. In addition, we will introduce a stochastic error component for modelling residual noise of the trajectory error function. <br><br> First step for error modelling requires to know the actual trajectory error values for several representative environments. In order to determine as accurately as possible the trajectories error, (almost) error less trajectories should be estimated using extracted nonsemantic features from a sequence of images collected with the terrestrial mobile mapping system and from a full set of ground control points. Once the references are estimated, they will be used to determine the actual errors in terrestrial mobile mapping trajectory. The rigorous analysis of these data sets will allow us to characterize the errors of a terrestrial mobile mapping system for a wide range of environments. This information will be of great use in future campaigns to improve the results of the 3D points cloud generation. <br><br> The proposed approach has been evaluated using real data. The data originate from a mobile mapping campaign over an urban and controlled area of Dortmund (Germany), with harmful GNSS conditions. The mobile mapping system, that includes two laser scanner and two cameras, was mounted on a van and it was driven over a controlled area around three hours. The results show the suitability to decompose trajectory error with non-continuous deterministic and stochastic components.

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 HYBRID MODELS FOR TRAJECTORY ERROR MODELLING IN URBAN ENVIRONMENTS

2016 ◽  
Vol XLI-B3 ◽  
pp. 805-811
Author(s):  
E. Angelatsa ◽  
M. E. Parés ◽  
I. Colomina
Keyword(s):  
Error Function ◽  
Real Data ◽  
Continuous Functions ◽  
Error Component ◽  
Urban Environments ◽  
Mobile Mapping ◽  
Trajectory Error ◽  
Mapping System ◽  
Wide Range ◽  
Mobile Mapping System

This paper tackles the first step of any strategy aiming to improve the trajectory of terrestrial mobile mapping systems in urban environments. We present an approach to model the error of terrestrial mobile mapping trajectories, combining deterministic and stochastic models. Due to urban specific environment, the deterministic component will be modelled with non-continuous functions composed by linear shifts, drifts or polynomial functions. In addition, we will introduce a stochastic error component for modelling residual noise of the trajectory error function. &lt;br&gt;&lt;br&gt; First step for error modelling requires to know the actual trajectory error values for several representative environments. In order to determine as accurately as possible the trajectories error, (almost) error less trajectories should be estimated using extracted nonsemantic features from a sequence of images collected with the terrestrial mobile mapping system and from a full set of ground control points. Once the references are estimated, they will be used to determine the actual errors in terrestrial mobile mapping trajectory. The rigorous analysis of these data sets will allow us to characterize the errors of a terrestrial mobile mapping system for a wide range of environments. This information will be of great use in future campaigns to improve the results of the 3D points cloud generation. &lt;br&gt;&lt;br&gt; The proposed approach has been evaluated using real data. The data originate from a mobile mapping campaign over an urban and controlled area of Dortmund (Germany), with harmful GNSS conditions. The mobile mapping system, that includes two laser scanner and two cameras, was mounted on a van and it was driven over a controlled area around three hours. The results show the suitability to decompose trajectory error with non-continuous deterministic and stochastic components.

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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