@article{jimaging9030072,

title = {6D Object Localization in Car-Assembly Industrial Environment},

author = {Alexandra Papadaki and Maria Pateraki},

url = {https://www.mdpi.com/2313-433X/9/3/72},

doi = {10.3390/jimaging9030072},

issn = {2313-433X},

year  = {2023},

date = {2023-01-04},

urldate = {2023-01-01},

journal = {Journal of Imaging},

volume = {9},

number = {3},

abstract = {In this work, a visual object detection and localization workflow integrated into a robotic platform is presented for the 6D pose estimation of objects with challenging characteristics in terms of weak texture, surface properties and symmetries. The workflow is used as part of a module for object pose estimation deployed to a mobile robotic platform that exploits the Robot Operating System (ROS) as middleware. The objects of interest aim to support robot grasping in the context of human-robot collaboration during car door assembly in industrial manufacturing environments. In addition to the special object properties, these environments are inherently characterised by cluttered background and unfavorable illumination conditions. For the purpose of this specific application, two different datasets were collected and annotated for training a learning-based method that extracts the object pose from a single frame. The first dataset was acquired in controlled laboratory conditions and the second in the actual indoor industrial environment. Different models were trained based on the individual datasets and a combination of them were further evaluated in a number of test sequences from the actual industrial environment. The qualitative and quantitative results demonstrate the potential of the presented method in relevant industrial applications.},

keywords = {challenging object characteristics, complex scenes, industrial robotic applications, Machine Learning, object 6D pose estimation, object localization},

pubstate = {published},

tppubtype = {article}

}

@article{potsiou2022prototype,

title = {A Prototype Machine Learning Tool Aiming to Support 3D Crowdsourced Cadastral Surveying of Self-Made Cities},

author = {Chryssy Potsiou and Nikolaos Doulamis and Nikolaos Bakalos and Maria Gkeli and Charalabos Ioannidis and Selena Markouizou},

url = {https://www.mdpi.com/2073-445X/12/1/8},

doi = {10.3390/land12010008},

issn = {2073-445X},

year  = {2022},

date = {2022-12-16},

urldate = {2022-12-16},

journal = {Land},

volume = {12},

number = {1},

pages = {8},

publisher = {MDPI},

abstract = {Land administration and management systems (LAMSs) have already made progress in the field of 3D Cadastre and the visualization of complex urban properties to support property markets and provide geospatial information for the sustainable management of smart cities. However, in less developed economies, with informally developed urban areas\textemdashthe so-called self-made cities\textemdashthe 2D LAMSs are left behind. Usually, they are less effective and mainly incomplete since a large number of informal constructions remain unregistered. This paper presents the latest results of an innovative on-going research aiming to structure, test and propose a low-cost but reliable enough methodology to support the simultaneous and fast implementation of both 2D land parcel and 3D property unit registration of informal, multi-story and unregistered constructions. An Indoor Positioning System (IPS) built upon low-cost Bluetooth technology combined with an innovative machine learning algorithm and connected with a 3D LADM-based cadastral mapping mobile application are the two key components of the technical solution under investigation. The proposed solution is tested for the first floor of a multi-room office building. The main conclusions concern the potential, usability and reliability of the method.},

keywords = {3D cadastre, 3D mapping, Crowdsourcing, indoor localization, informal development, Machine Learning},

pubstate = {published},

tppubtype = {article}

}

@article{Antoniou2020,

title = {A deep learning method to accelerate the disaster response process},

author = {Vyron Antoniou and Chryssy Potsiou},

url = {https://www.unisdr.org/},

doi = {10.3390/rs12030544},

issn = {20724292},

year  = {2020},

date = {2020-02-01},

journal = {Remote Sens.},

volume = {12},

number = {3},

pages = {544},

publisher = {MDPI AG},

abstract = {This paper presents an end-to-end methodology that can be used in the disaster response process. The core element of the proposed method is a deep learning process which enables a helicopter landing site analysis through the identification of soccer fields. The method trains a deep learning autoencoder with the help of volunteered geographic information and satellite images. The process is mostly automated, it was developed to be applied in a time-and resource-constrained environment and keeps the human factor in the loop in order to control the final decisions. We show that through this process the cognitive load (CL) for an expert image analyst will be reduced by 70%, while the process will successfully identify 85.6% of the potential landing sites. We conclude that the suggested methodology can be used as part of a disaster response process.},

keywords = {artificial intelligence, deep learning, Deep learning autoencoder, Disaster response management, Helicopter landing site analysis, Machine Learning, Object detection, Satellite imagery, Volunteered geographic information},

pubstate = {published},

tppubtype = {article}

}

@article{Agrafiotis2020,

title = {Correcting Image Refraction: Towards Accurate Aerial Image-Based Bathymetry Mapping in Shallow Waters},

author = {Panagiotis Agrafiotis and Konstantinos Karantzalos and Andreas Georgopoulos and Dimitrios Skarlatos},

url = {https://doi.org/10.3390/rs12020322 https://www.mdpi.com/2072-4292/12/2/322},

doi = {10.3390/rs12020322},

issn = {2072-4292},

year  = {2020},

date = {2020-01-01},

journal = {Remote Sens.},

volume = {12},

number = {2},

pages = {322},

publisher = {MDPI AG},

abstract = {Although aerial image-based bathymetric mapping can provide, unlike acoustic or LiDAR (Light Detection and Ranging) sensors, both water depth and visual information, water refraction poses significant challenges for accurate depth estimation. In order to tackle this challenge, we propose an image correction methodology, which first exploits recent machine learning procedures that recover depth from image-based dense point clouds and then corrects refraction on the original imaging dataset. This way, the structure from motion (SfM) and multi-view stereo (MVS) processing pipelines are executed on a refraction-free set of aerial datasets, resulting in highly accurate bathymetric maps. Performed experiments and validation were based on datasets acquired during optimal sea state conditions and derived from four different test-sites characterized by excellent sea bottom visibility and textured seabed. Results demonstrated the high potential of our approach, both in terms of bathymetric accuracy, as well as texture and orthoimage quality.},

keywords = {Aerial imagery, Bathymetry, Coastal mapping, DSM, Image correction, Machine Learning, Refraction correction, Seabed Mapping, SfM, UAV},

pubstate = {published},

tppubtype = {article}

}

@article{Agrafiotis2019a,

title = {DepthLearn: Learning to correct the refraction on point clouds derived from aerial imagery for accurate dense shallow water bathymetry based on SVMs-fusion with LiDAR point clouds},

author = {Panagiotis Agrafiotis and Dimitrios Skarlatos and Andreas Georgopoulos and Konstantinos Karantzalos},

url = {https://www.mdpi.com/2072-4292/11/19/2225},

doi = {10.3390/rs11192225},

issn = {20724292},

year  = {2019},

date = {2019-09-01},

journal = {Remote Sens.},

volume = {11},

number = {19},

pages = {2225},

publisher = {MDPI AG},

abstract = {The determination of accurate bathymetric information is a key element for near offshore activities; hydrological studies, such as coastal engineering applications, sedimentary processes, hydrographic surveying, archaeological mapping and biological research. Through structure from motion (SfM) and multi-view-stereo (MVS) techniques, aerial imagery can provide a low-cost alternative compared to bathymetric LiDAR (Light Detection and Ranging) surveys, as it offers additional important visual information and higher spatial resolution. Nevertheless, water refraction poses significant challenges on depth determination. Till now, this problem has been addressed through customized image-based refraction correction algorithms or by modifying the collinearity equation. In this article, in order to overcome the water refraction errors in a massive and accurate way, we employ machine learning tools, which are able to learn the systematic underestimation of the estimated depths. In particular, an SVR (support vector regression) model was developed, based on known depth observations from bathymetric LiDAR surveys, which is able to accurately recover bathymetry from point clouds derived from SfM-MVS procedures. Experimental results and validation were based on datasets derived from different test-sites, and demonstrated the high potential of our approach. Moreover, we exploited the fusion of LiDAR and image-based point clouds towards addressing challenges of both modalities in problematic areas.},

keywords = {Aerial imagery, Bathymetry, Data integration, Fusion, LiDAR, Machine Learning, Point Cloud, Refraction effect, Seabed Mapping, SVM, UAV},

pubstate = {published},

tppubtype = {article}

}

@article{Agrafiotis2019,

title = {SHALLOW WATER BATHYMETRY MAPPING from UAV IMAGERY BASED on MACHINE LEARNING},

author = {P Agrafiotis and D Skarlatos and A Georgopoulos and K Karantzalos},

url = {https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLII-2-W10/9/2019/},

doi = {10.5194/isprs-archives-XLII-2-W10-9-2019},

issn = {21949050},

year  = {2019},

date = {2019-04-01},

journal = {ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci.},

volume = {42},

number = {2/W10},

pages = {9--16},

abstract = {The determination of accurate bathymetric information is a key element for near offshore activities, hydrological studies such as coastal engineering applications, sedimentary processes, hydrographic surveying as well as archaeological mapping and biological research. UAV imagery processed with Structure from Motion (SfM) and Multi View Stereo (MVS) techniques can provide a low-cost alternative to established shallow seabed mapping techniques offering as well the important visual information. Nevertheless, water refraction poses significant challenges on depth determination. Till now, this problem has been addressed through customized image-based refraction correction algorithms or by modifying the collinearity equation. In this paper, in order to overcome the water refraction errors, we employ machine learning tools that are able to learn the systematic underestimation of the estimated depths. In the proposed approach, based on known depth observations from bathymetric LiDAR surveys, an SVR model was developed able to estimate more accurately the real depths of point clouds derived from SfM-MVS procedures. Experimental results over two test sites along with the performed quantitative validation indicated the high potential of the developed approach.},

keywords = {Bathymetry, Machine Learning, Point Cloud, Refraction effect, Seabed Mapping, SVM, UAV},

pubstate = {published},

tppubtype = {article}

}