Colleagues

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    Laboratory of Robotics and Automation (LRA)

    Since 2013 I am a proud member of the Laboratory of Robotics and Automation, where along with my colleagues, I have been employed in some very interesting research activities.

    The Laboratory of Robotics and Automation performs and promotes research in application problems that arise in the area of robotics, computer vision, navigation, multimodal integration, image analysis and understanding, visual surveillance, intelligent sensory networks, sensory fusion, and other relative topics.

    The lab utilizes state of the art tools to expand the scientific and technological front in the respective research areas, namely: artificial vision (including cognitive and robot Vision); intelligent systems (such as artificial neural networks); pattern recognition.

Research Activities

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    Funded scholarship by Google's Project Tango

    A platform that uses computer vision to give mobile devices the ability to understand their position relative to the world around them.

    During my scholarship with Professor Stergios Roumeliotis (member of my advisor committee) at his lab in University of Minneapolis (MarsLab), I contributed to his research activities by implementing a variety of accelerated versions of Computer Vision and Estimation Theory algorithms on GP-GPU and SIMD-CPU systems. Additionally, I had the chance to study some of the most interesting subjects of my research area like Robotics, Probability, Linear Algebra and Estimation theory.

    Moreover, I participated in Google’s Project Tango, which achieved to create a mobile device capable of localizing itself and estimating the structure of its surrounding world in real time for indoor environments where GPS measurements are not available. Instead, this was achieved by using sensors like RGB/RGBD cameras, accelerometers, gyroscopes and many others low-cost sensors, all of them included in one device.

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    The Hellenic Civil Unmanned Air Vehicle (HCUAV) project

    The first aerial control, prevention and alert system in Greece.

    I have worked on the HCUAV project, run by the research committee of the Democritus University of Thrace. This project developed a surveillance Medium-Altitude Long-Endurance UAV (MALE UAV) capable of flying autonomously for long distances while creating an orthorectified and georeferenced map of the world underneath.

    My task in this project included the creation of the aforementioned 3D map and the detection of ambiguous events. In order to carry out those requirements SLAM and Deep Learning techniques need to be used, while having in mind a real-time implementation.

    11SYNERGASIA_9_629, National Strategic Reference Framework (NSRF) – Competitiveness & Entrepreneurship – SYNERGASIA 2011

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    The Autonomous Vehicle Emergency Recovery Tool (AVERT) project

    Automated deployment, extraction and removal of blocking and suspect vehicles.

    I have also worked on the AVERT project. This project developed a security system capable of removing any blocking or suspect vehicle from vulnerable positions such as enclosed infrastructure spaces, tunnels, low bridges as well as under-building and underground car parks. Vehicles can be removed from confined spaces with delicate handling, swiftly, and in any direction to a safer disposal point to reduce or eliminate collateral damage to infrastructure and personnel. Remote operation, self-powered and onboard sensors provide a new capability that can operate alongside existing technologies, thereby enhancing bomb disposal response, speed and safety.

    My task in this project included the development of place recognition algorithms in order for the system to be able to recover its position in localization failure scenarios.

    FP7-SEC-2011-1-285092, European Commission – Information & Communication Technologies (ICT).

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    NOvelty or Anomaly Hunter (NOAH) project

    Identifying unusual structures in Mars-like environments.

    I am currently working on the NOAH project for the European Space Agency (ESA). The NOAH activity aims to improve the state-of-the-art novelty detection techniques specifically for Mars-like environments.

    My effort to this project refers to the investigation of novel CNN-based techniques for segmenting and classifying regions of interest.

    European Space Agency T713-503MM

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Encoding the Description of Image Sequences: A Two-Layered Pipeline for Loop Closure Detection

Loukas Bampis, Angelos Amanatiadis, Antonios Gasteratos
Conference Paper IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2016, Pages 4530-4536.

Abstract

In this paper we propose a novel technique for detecting loop closures on a trajectory by matching sequences of images instead of single instances. We build upon well established techniques for creating a bag of visual words with a tree structure and we introduce a significant novelty by extending these notions to describe the visual information of entire regions using Visual-Word-Vectors. The fact that the proposed approach does not rely on a single image to recognize a site allows for a more robust place recognition, and consequently loop closure detection, while reduces the computational complexity for long trajectory cases. We present evaluation results for multiple publicly available indoor and outdoor datasets using Precision-Recall curves, which reveal that our method outperforms other state of the art algorithms.

A LoCATe-based Visual Place Recognition System for Mobile Robotics and GPGPUs

Loukas Bampis, Savvas A. Chatzichristofis, Chrisa Iakovidou, Angelos Amanatiadis, Yiannis S. Boutalis and Antonios Gasteratos
Journal Paper Concurrency and Computation: Practice and Experience (accepted for publication), 2017, Pages -.

Abstract

In this paper, a novel visual Place Recognition (vPR) approach is evaluated based on a visual vocabulary of the Color and Edge Directivity Descriptor (CEDD) in order to address the loop closure detection task. Even though CEDD was initially designed so as to globally describe the color and texture information of an input image addressing Image Indexing and Retrieval tasks, its scalability on characterizing single feature points has already been proven. Thus, instead of using CEDD as a global descriptor, we adopt a bottom-up approach and utilize its localized version, Local Color And Texture dEscriptor (LoCATe), as an input to a state-of-the-art visual Place Recognition technique based on Visual Word Vectors. Also, we employ a parallel execution pipeline based on a previous work of ours using the well-established GPGPU computing. Our experiments show that the usage of CEDD as a local descriptor produces high accuracy vPR results, while the parallelization employed allows for a real-time implementation even in the case of a low-cost mobile device.

Accelerating single-image super-resolution polynomial regression in mobile devices

Angelos Amanatiadis, Loukas Bampis, Antonios Gasteratos
Journal Paper Transactions on Consumer Electronics<, Volume 61, Number 1, 2015, Pages 63-71.

Abstract

This paper introduces a new super-resolution algorithm based on machine learning along with a novel hybrid implementation for next generation mobile devices. The proposed super-resolution algorithm entails a two dimensional polynomial regression method using only the input image properties for the learning task. Model selection is applied for defining the optimal degree of polynomial by adopting regularization capability in order to avoid overfitting. Although it is widely believed that machine learning algorithms are not appropriate for real-time implementation, the paper in hand proves that there are indeed specific hypothesis representations that are able to be integrated into real-time mobile applications. With aim to achieve this goal, the increasing GPU employment in modern mobile devices is exploited. More precisely, by utilizing the mobile GPU as a co-processor in a hybrid pipelined implementation, significant performance speedup along with superior quantitative results can be achieved.

Real-time surveillance detection system for medium-altitude long-endurance unmanned aerial vehicles

Angelos Amanatiadis, Loukas Bampis, Evangelos G. Karakasis, Antonios Gasteratos and Georgios Ch. Sirakoulis
Journal Paper Concurrency and Computation: Practice and Experience, 2017, Pages -.

Abstract

The detection of ambiguous objects, although challenging, is of great importance for any surveillance system and especially for an Unmanned Aerial Vehicle (UAV), where the measurements are affected by the great observing distance. Wildfire outbursts and illegal migration are only some of the examples that such a system should distinguish and report to the appropriate authorities. More specifically, Southern European countries commonly suffer from those problems due to the mountainous terrain and thick forests that contain. UAVs like the "Hellenic Civil Unmanned Air Vehicle - HCUAV" project have been designed in order to address high altitude detection tasks and patrol the borders and woodlands for any ambiguous activity. In this paper, a moment-based blob detection approach is proposed that utilizes the thermal footprint obtained from single infrared (IR) images and distinguishes human or fire sized and shaped figures. Our method is specifically designed so as to be appropriately integrated into hardware acceleration devices, such as GPGPUs and FPGAs, and takes full advantage of their respective parallelization capabilities succeeding real-time performances and energy efficiency. The timing evaluation of the proposed hardware accelerated algorithm's adaptations shows an achieved speedup of up to 7 times, as compared to a highly optimized CPU-only based version.

Exploring CNN Based Anomaly Detection for Robotic Mars Exploration Missions

Loukas Bampis, Antonios Gasteratos, Evangelos Boukas
Abstract Paper 14th Symposium on Advanced Space Technologies in Robotics and Automation (ASTRA), 2017, Pages -.

Abstract

A visual anomaly detection algorithm typically incorporates two modules, namely the Region Of Interest (ROI) detection and the ROI characterization. During the first module, salient image regions are identified containing semantically distinctive entities, while the second one refers to the characterization of the detected ROIs as "novel" (unable of being classified) or "known" (capable of being classified into one of the pre-trained classes). In this paper, we are interested in investigating a unified solution that treats the ROI detection and characterization functionalities as a single task using the classification properties of the Convolutional Neural Networks (CNNs).

Can Speedup Assist Accuracy? An on-board GPU-Accelerated Image Georeference Method for UAVs

Loukas Bampis, Evangelos G. Karakasis, Angelos Amanatiadis, Antonios Gasteratos
Conference Paper International Conference on Computer Vision Systems, 2015, Pages 104-114.

Abstract

This paper presents a georeferenced map extraction method, for Medium-Altitude Long-Endurance UAVs. The adopted technique of projecting world points to an image plane is a perfect candidate for a GPU implementation. The achieved high frame rate leads to a plethora of measurements even in the case of a low-power mobile processing unit. These measurements can later be combined in order to refine the output and create a more accurate result.

The HCUAV project: Electronics and software development for medium altitude remote sensing

Angelos Amanatiadis, Evangelos G. Karakasis, Loukas Bampis, Themis Giitsidis, Periklis Panagiotou, Georgios Ch. Sirakoulis, Antonios Gasteratos, Philippos Tsalides, Alexandros Goulas, Kyriakos Yakinthos
Conference Paper International Symposium on Safety, Security, and Rescue Robotics, 2014, Pages 1-5.

Abstract

The continuous increase of illegal migration flows to southern European countries has been recently in the spotlight of European Union due to numerous deadly incidents. Another common issue that the aforementioned countries share is the Mediterranean wildfires which are becoming more frequent due to the warming climate and increasing magnitudes of droughts. Different ground early warning systems have been funded and developed across these countries separately for these incidents, however they have been proved insufficient mainly because of the limited surveyed areas and challenging Mediterranean shoreline and landscape. In 2011, the Greek Government along with European Commission, decided to support the development of the first Hellenic Civil Unmanned Aerial Vehicle (HCUAV), which will provide solutions to both illegal migration and wildfires. This paper presents the challenges in the electronics and software design, and especially the under development solutions for detection of human and fire activity, image mosaicking and orthorectification using commercial off-the-shelf sensors. Preliminary experimental results of the HCUAV medium altitude remote sensing algorithms, show accurate and adequate results using low cost sensors and electronic devices.

Real-time indexing for large image databases: Color and Edge Directivity Descriptor on GPU

Loukas Bampis, Chrisa Iakovidou, Savvas A. Chatzichristofis, Yiannis S. Boutalis
Journal Paper The Journal of Supercomputing, Volume 71, Number 3, 2015, Pages 909-937.

Abstract

In this paper, we focus on implementing the extraction of a well-known low-level image descriptor using the multicore power provided by general-purpose graphic processing units (GPGPUs). The color and edge directivity descriptor, which incorporates both color and texture information achieving a successful trade-off between effectiveness and efficiency, is employed and reassessed for parallel execution. We are motivated by the fact that image/frame indexing should be achieved real time, which in our case means that a system should be capable of indexing a frame or an image as it becomes part of a database (ideally, calculating the descriptor as the images are captured). Two strategies are explored to accelerate the method and bypass resource limitations and architectural constrains. An approach that exclusively uses the GPU together with a hybrid implementation that distributes the computations to both available GPU and CPU resources are proposed. The first approach is strongly based on the compute unified device architecture and excels compared to all other solutions when the GPU resources are abundant. The second implementation suggests a hybrid scheme where the extraction process is split in two sequential stages, allowing the input data (images or video frames) to be pipelined through the central and the graphic processing units. Experimental results were conducted on four different combinations of GPU–CPU technologies in order to highlight the strengths and the weaknesses of all implementations. Real-time indexing is obtained over all computational setups for both GPU-only and Hybrid techniques. An impressive 22 times acceleration is recorded for the GPU-only method. The proposed Hybrid implementation outperforms the GPU-only implementation and becomes the preferred solution when a low-cost setup (i.e., more advanced CPU combined with a relatively weak GPU) is employed.

Image Encryption Using the Recursive Attributes of the eXclusive-OR Filter

Savvas A. Chatzichristofis, Loukas Bampis, Oge Marques, Mathias Lux, Yiannis Boutalis
Journal Paper Journal of Cellular Automata, Volume 9, Number 2-3, 2014, Pages 125-137

Abstract

This paper describes a visual multimedia content encryption approach based on cellular automata (CA), expanding the work proposed in [4]. The presented algorithm relies on an attribute of the eXclusive-OR (XOR) filter, according to which, the original content of a cellular neighborhood can be reconstructed following a predefined number of applications of the filter. During the interim time marks, the cellular neighborhood is greatly distorted, making it impossible to be recognized. The application of this attribute to the field of image processing, results to a strong visual multimedia content encryption approach. Additionally, this paper proposes a new approach for the acceleration of the application of the XOR-filter, taking advantage of the Summed Area Tables (SAT) approach.

Digital elevation model fusion using spectral methods

Evangelos G. Karakasis, Loukas Bampis, Angelos Amanatiadis, Antonios Gasteratos, Philippos Tsalides
Conference Papers International Conference on Imaging Systems and Techniques, 2014, Pages 340-345.

Abstract

This paper presents the application of different spectral methods, like Fourier series and polynomial-based expansions, to Digital Elevation Models (DEMs) in order to fuse their content. Two different fusion techniques: 1) a filter-based one and 2) a weighted average of expansion coefficients, are examined. Their performance is evaluated by using both ground-truth lidar data as well as fusion quality measures. The results point out that polynomial-based spectral expansions perform better than the traditional Fourier approach.

Accelerating image super-resolution regression by a hybrid implementation in mobile devices

Angelos Amanatiadis, Loukas Bampis, Antonios Gasteratos
Conference Paper International Conference on Consumer Electronics, 2014, Pages 335-336.

Abstract

This paper introduces a new super-resolution algorithm based on machine learning along with a novel hybrid implementation for next generation mobile devices. The proposed super-resolution algorithm entails a multivariate polynomial regression method using only the input image properties for the learning task. Although it is widely believed that machine learning algorithms are not appropriate for real-time implementation, the paper in hand proves that there are indeed specific hypothesis representations that are able to be integrated into real-time mobile applications. With aim to achieve this goal, we take advantage of the increasing GPU employment in modern mobile devices. More precisely, we utilize the mobile GPU as a co-processor in a hybrid pipelined implementation achieving significant performance speedup along with superior quantitative interpolation results.

Color and Edge Directivity Descriptor on GPGPU

Chrisa Iakovidou, Loukas Bampis, Savvas A. Chatzichristofis, Yiannis S. Boutalis, Angelos Amanatiadis
Conference Paper International Conference on Parallel, Distributed, and Network-Based Processing, 2015, Pages 301-308.

Abstract

Image indexing refers to describing the visual multimedia content of a medium, using high level textual information or/and low level descriptors. In most cases, images and videos are associated with noisy and incomplete user-supplied textual annotations, possibly due to omission or the excessive cost associated with the metadata creation. In such cases, Content Based Image Retrieval (CBIR) approaches are adopted and low level image features are employed for indexing and retrieval. We employ the Colour and Edge Directivity Descriptor (CEDD), which incorporates both colour and texture information in a compact representation and reassess it for parallel execution, utilizing the multicore power provided by General Purpose Graphic Processing Units (GPGPUs). Experiments conducted on four different combinations of GPU-CPU technologies revealed an impressive gained acceleration when using a GPU, which was up to 22 times faster compared to the respective CPU implementation, while real-time indexing was achieved for all tested GPU models.

Reviewer

Together with my ongoing research, I have also contributed to the scientific community as a reviewer on a variety of conference and journal papers for IROS, ICRA, Electronics Letters and more.

Teaching Assistant

I have co-taught the following courses:
  • 2017 2016

    Robotics Vision

    First Semester

  • 2017 2016

    Robotics

    Second Semester

  • 2017 2016

    Control Systems

    First Semester

  • 2016 2015

    Robotics

    Second Semester

  • 2016 2015

    Control Systems

    First Semester

  • 2016 2015

    Mechatronics

    First Semester

  • 2016 2015

    Electronics

    Second Semester

  • 2015 2014

    Electronics

    Second Semester

  • 2014 2013

    Electronics

    Second Semester

Supervision of Diploma Theses

I have also supervised the following diploma theses:
  • Present 2017

    Despoina Mati

    Vehicle passengers detection using CNNs.

  • Present 2015

    Aliki Makri

    Landmark recognition using visual vocabularies.

  • Present 2016

    Vasiliki Balaska

    Visual Inertial SLAM for multiple quadcopters.

  • Present 2016

    Anatoli Tziola

    A comparative study between local descriptors for place recognition tasks.

  • 2017 2016

    Vasilis Liatsos

    An 8-point algorithm SLAM for freely moving cameras.

  • 2017 2016

    Maria Kyrou

    Loop closure detection for indoor/ourdoor applications.

Teaching to primary school

  • 2016 2015

    Robotics

    I have taught a class of primary school students about the basic principles of Robotics using the educational toolkit provided by Lego Mindstorms. That was probably the most challenging and interesting course that I have experienced so far. It is really exciting to witness such young minds to understand and use concepts from robotics theory, like a frame of reference, while I also learned a lot about the overall teaching procedure.