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  • 1.
    Ahlberg, Carl
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Embedded high-resolution stereo-vision of high frame-rate and low latency through FPGA-acceleration2020Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    Autonomous agents rely on information from the surrounding environment to act upon. In the array of sensors available, the image sensor is perhaps the most versatile, allowing for detection of colour, size, shape, and depth. For the latter, in a dynamic environment, assuming no a priori knowledge, stereo vision is a commonly adopted technique. How to interpret images, and extract relevant information, is referred to as computer vision. Computer vision, and specifically stereo-vision algorithms, are complex and computationally expensive, already considering a single stereo pair, with results that are, in terms of accuracy, qualitatively difficult to compare. Adding to the challenge is a continuous stream of images, of a high frame rate, and the race of ever increasing image resolutions. In the context of autonomous agents, considerations regarding real-time requirements, embedded/resource limited processing platforms, power consumption, and physical size, further add up to an unarguably challenging problem.

    This thesis aims to achieve embedded high-resolution stereo-vision of high frame-rate and low latency, by approaching the problem from two different angles, hardware and algorithmic development, in a symbiotic relationship. The first contributions of the thesis are the GIMME and GIMME2 embedded vision platforms, which offer hardware accelerated processing through FGPAs, specifically targeting stereo vision, contrary to available COTS systems at the time. The second contribution, toward stereo vision algorithms, is twofold. Firstly, the problem of scalability and the associated disparity range is addressed by proposing a segment-based stereo algorithm. In segment space, matching is independent of image scale, and similarly, disparity range is measured in terms of segments, indicating relatively few hypotheses to cover the entire range of the scene. Secondly, more in line with the conventional stereo correspondence for FPGAs, the Census Transform (CT) has been identified as a recurring cost metric. This thesis proposes an optimisation of the CT through a Genetic Algorithm (GA) - the Genetic Algorithm Census Transform (GACT). The GACT shows promising results for benchmark datasets, compared to established CT methods, while being resource efficient.

  • 2.
    Ahlberg, Carl
    et al.
    Mälardalen University, School of Innovation, Design and Engineering. Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Asplund, Lars
    Mälardalen University, School of Innovation, Design and Engineering. Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Campeanu, Gabriel
    Mälardalen University, School of Innovation, Design and Engineering. Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Ciccozzi, Federico
    Mälardalen University, School of Innovation, Design and Engineering. Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Ekstrand, Fredrik
    Mälardalen University, School of Innovation, Design and Engineering. Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Ekström, Mikael
    Mälardalen University, School of Innovation, Design and Engineering. Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Feljan, Juraj
    Mälardalen University, School of Innovation, Design and Engineering. Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Gustavsson, Andreas
    Mälardalen University, School of Innovation, Design and Engineering. Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Sentilles, Séverine
    Mälardalen University, School of Innovation, Design and Engineering. Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Svogor, Ivan
    FOI, University of Zagreb.
    Segerblad, Emil
    The Black Pearl: An Autonomous Underwater Vehicle2013Report (Other academic)
    Abstract [en]

    The Black Pearl is a custom made autonomous underwater vehicle developed at Mälardalen University, Sweden. It is built in a modular fashion, including its mechanics, electronics and software. After a successful participation at the RoboSub competition in 2012 and winning the prize for best craftsmanship, this year we made minor improvements to the hardware, while the focus of the robot's evolution shifted to the software part. In this paper we give an overview of how the Black Pearl is built, both from the hardware and software point of view.

  • 3.
    Ahlberg, Carl
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Ekstrand, Fredrik
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Ekström, Mikael
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Spampinato, Giacomo
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Asplund, Lars
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    GIMME2 - An embedded system for stereo vision and processing of megapixel images with FPGA-acceleration2015In: 2015 International Conference on ReConFigurable Computing and FPGAs, ReConFig 2015, 2015Conference paper (Refereed)
    Abstract [en]

    This paper presents GIMME2, an embedded stereovision system, designed to be compact, power efficient, cost effective, and high performing in the area of image processing. GIMME2 features two 10 megapixel image sensors and a Xilinx Zynq, which combines FPGA-fabric with a dual-core ARM CPU on a single chip. This enables GIMME2 to process video-rate megapixel image streams at real-time, exploiting the benefits of heterogeneous processing.

  • 4.
    Ahlberg, Carl
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Leon, Miguel
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Ekstrand, Fredrik
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Ekström, Mikael
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    The Genetic Algorithm Census TransformManuscript (preprint) (Other academic)
  • 5.
    Ahlberg, Carl
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Leon, Miguel
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Ekstrand, Fredrik
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Ekström, Mikael
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Unbounded Sparse Census Transform using Genetic Algorithm2019In: 2019 IEEE WINTER CONFERENCE ON APPLICATIONS OF COMPUTER VISION (WACV), IEEE , 2019, p. 1616-1625Conference paper (Refereed)
    Abstract [en]

    The Census Transform (CT) is a well proven method for stereo vision that provides robust matching, with respect to object boundaries, outliers and radiometric distortion, at a low computational cost. Recent CT methods propose patterns for pixel comparison and sparsity, to increase matching accuracy and reduce resource requirements. However, these methods are bounded with respect to symmetry and/or edge length. In this paper, a Genetic algorithm (GA) is applied to find a new and powerful CT method. The proposed method, Genetic Algorithm Census Transform (GACT), is compared with the established CT methods, showing better results for benchmarking datasets. Additional experiments have been performed to study the search space and the correlation between training and evaluation data.

  • 6.
    Ahlberg, Carl
    et al.
    Mälardalen University, School of Innovation, Design and Engineering.
    Lidholm, Jörgen
    Mälardalen University, School of Innovation, Design and Engineering.
    Ekstrand, Fredrik
    Mälardalen University, School of Innovation, Design and Engineering.
    Spampinato, Giacomo
    Mälardalen University, School of Innovation, Design and Engineering.
    Ekström, Mikael
    Mälardalen University, School of Innovation, Design and Engineering.
    Asplund, Lars
    Mälardalen University, School of Innovation, Design and Engineering.
    GIMME - A General Image Multiview Manipulation Engine2011In: Proceedings of the International Conference on ReConFigurable Computing and FPGAs (ReConFig 2011), Los Alamitos, Calif: IEEE Computer Society, 2011Conference paper (Refereed)
    Abstract [en]

    This paper presents GIMME (General Image Multiview Manipulation Engine), a highly flexible reconfigurable stand-alone mobile two-camera vision platform with stereo-vision capability. GIMME relies on reconfigurable hardware (FPGA) to perform application-specific low to medium-level image-processing at video rate. The Qseven-extension enables additional processing power. Thanks to its compact design, low power consumption and standardized interfaces (power and communication), GIMME is an ideal vision platform for autonomous and mobile robot applications.

  • 7.
    Ekstrand, Fredrik
    et al.
    Mälardalen University, School of Innovation, Design and Engineering.
    Ahlberg, Carl
    Mälardalen University, School of Innovation, Design and Engineering.
    Ekström, Mikael
    Mälardalen University, School of Innovation, Design and Engineering.
    Asplund, Lars
    Mälardalen University, School of Innovation, Design and Engineering.
    Spampinato, Giacomo
    Mälardalen University, School of Innovation, Design and Engineering.
    Resource Limited Hardware-based Stereo Matching for High-Speed Vision System2011In: ICARA 2011 - Proceedings of the 5th International Conference on Automation, Robotics and Applications, 2011, p. 465-469Conference paper (Refereed)
    Abstract [en]

    This paper proposes a limited implementation of areabasedstereo matching for minimal resource utilization. It shows that it is possible to achieve an acceptable disparity map without the use of expensive resources. The matching accuracy for the single-row SAD can even outperform that of its full-row counterpart. Additionally, it excels in terms of frame rate and resource utilization, and is highly suitable for real-time stereo-vision systems.

  • 8.
    Ekstrand, Fredrik
    et al.
    Mälardalen University, School of Innovation, Design and Engineering.
    Ahlberg, Carl
    Ekström, Mikael
    Asplund, Lars
    Spampinato, Giacomo
    Utilization and Performance Considerations in Resource Optimized Stereo Matching for Real-Time Reconfigurable Hardware2012In: VISAPP 2012 - Proceedings of the International Conference on Computer Vision Theory and Application, vol. 2, 2012, p. 415-418Conference paper (Other academic)
    Abstract [en]

    This paper presents a set of approaches for increasing the accuracy of basic area-based stereo matching methods. It is targeting real-time FPGA systems for dense disparity map estimation. The methods are focused on low resource usage and maximized improvement per cost unit to enable the inclusion of an autonomous system in an FPGA. The approach performs on par with other area-matching implementations, but at substantially lower resource usage. Additionally, the solution removes the requirement for external memory for reconfigurable hardware together with the limitation in image size accompanying standard methods. As a fully piped complete on-chip solution, it is highly suitable for real-time stereo-vision systems, with a frame rate over 100 fps for Megapixel images.

  • 9.
    Ekstrand, Fredrik
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Ahlberg, Carl
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Ekström, Mikael
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Spampinato, Giacomo
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    High-speed segmentation-driven high-resolution matching2015In: Proceedings of SPIE - The International Society for Optical Engineering, vol. 9445, 2015, Vol. 9445, p. Article number 94451Y-Conference paper (Refereed)
    Abstract [en]

    This paper proposes a segmentation-based approach for matching of high-resolution stereo images in real time. The approach employs direct region matching in a raster scan fashion influenced by scanline approaches, but with pixel decoupling. To enable real-time performance it is implemented as a heterogeneous system of an FPGA and a sequential processor. Additionally, the approach is designed for low resource usage in order to qualify as part of unified image processing in an embedded system.

  • 10.
    Ekstrand, Fredrik
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Ahlberg, Carl
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Ekström, Mikael
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Spampinato, Giacomo
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Towards an Embedded Real-Time High Resolution Vision System2014In: ADVANCES IN VISUAL COMPUTING (ISVC 2014), PT II / [ed] Bebis, G Boyle, R Parvin, B Koracin, D McMahan, R Jerald, J Zhang, H Drucker, SM Kambhamettu, C ElChoubassi, M Deng, Z Carlson, M, SPRINGER-VERLAG BERLIN , 2014, p. 541-550Conference paper (Refereed)
    Abstract [en]

    This paper proposes an approach to image processing for high performance vision systems. Focus is on achieving a scalable method for real-time disparity estimation which can support high resolution images and large disparity ranges. The presented implementation is a non-local matching approach building on the innate qualities of the processing platform which, through utilization of a heterogeneous system, combines low-complexity approaches into performing a high-complexity task. The complementary platform composition allows for the FPGA to reduce the amount of data to the CPU while at the same time promoting the available informational content, thus both reducing the workload as well as raising the level of abstraction. Together with the low resource utilization, this allows for the approach to be designed to support advanced functionality in order to qualify as part of unified image processing in an embedded system.

  • 11.
    Loni, Mohammad
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Ahlberg, Carl
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Daneshtalab, Masoud
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Ekström, Mikael
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Sjödin, Mikael
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Embedded Acceleration of Image Classification Applications for Stereo Vision Systems2018In: Design, Automation & Test in Europe Conference & Exhibition DATE'18, 2018Conference paper (Other academic)
  • 12.
    Spampinato, Giacomo
    et al.
    Mälardalen University, School of Innovation, Design and Engineering.
    Lidholm, Jörgen
    Mälardalen University, School of Innovation, Design and Engineering.
    Ahlberg, Carl
    Mälardalen University, School of Innovation, Design and Engineering.
    Ekstrand, Fredrik
    Mälardalen University, School of Innovation, Design and Engineering.
    Ekström, Mikael
    Mälardalen University, School of Innovation, Design and Engineering.
    Asplund, Lars
    Mälardalen University, School of Innovation, Design and Engineering.
    An Embedded Stereo Vision Module for 6D Pose Estimation and Mapping2011In: Proceedings of the IEEE international conference on Intelligent Robots and Systems IROS2011, New York: IEEE Press, 2011, p. 1626-1631Conference paper (Refereed)
    Abstract [en]

    This paper presents an embedded vision system based on reconfigurable hardware (FPGA) and two CMOS cameras to perform stereo image processing and 3D mapping for autonomous navigation. We propose an EKF based visual SLAM and sparse feature detectors to achieve 6D localization of the vehicle in non flat scenarios. The system can operate regardless of the odometry information from the vehicle since visual odometry is used. As a result, the final system is compact and easy to install and configure.

  • 13.
    Spampinato, Giacomo
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Lidholm, Jörgen
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Ahlberg, Carl
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Ekstrand, Fredrik
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Ekström, Mikael
    Mälardalen University, School of Innovation, Design and Engineering.
    Asplund, Lars
    An embedded stereo vision module for industrial vehicles automation2013In: Proceedings of the IEEE International Conference on Industrial Technology, IEEE , 2013, p. 52-57Conference paper (Refereed)
    Abstract [en]

    This paper presents an embedded vision system based on reconfigurable hardware (FPGA) to perform stereo image processing and 3D mapping of sparse features for autonomous navigation and obstacle detection in industrial settings. We propose an EKF based visual SLAM to achieve a 6D localization of the vehicle even in non flat scenarios. The system uses vision as the only source of information. As a consequence, it operates regardless of the odometry from the vehicle since visual odometry is used. © 2013 IEEE.

  • 14.
    Spampinato, Giacomo
    et al.
    Mälardalen University, School of Innovation, Design and Engineering.
    Lidholm, Jörgen
    Mälardalen University, School of Innovation, Design and Engineering.
    Ekstrand, Fredrik
    Mälardalen University, School of Innovation, Design and Engineering.
    Ahlberg, Carl
    Mälardalen University, School of Innovation, Design and Engineering.
    Ekström, Mikael
    Mälardalen University, School of Innovation, Design and Engineering.
    Asplund, Lars
    Mälardalen University, School of Innovation, Design and Engineering.
    Navigation in a box: Stereovision for industry automation2010In: Advances in Theory and Applications of Stereo Vision / [ed] Asim Bhatti, InTech , 2010Chapter in book (Other academic)
    Abstract [en]

    The research presented addresses the emerging topic of AGVs (Automated Guided Vehicles) specifically related to industrial sites. The work presented has been carried out in the frame of the MALTA project (Multiple Autonomous forklifts for Loading and Transportation Applications), a joint research project between industry and university, funded by the European Regional Development and Robotdalen, in partnership with theSwedish Knowledge Foundation. The project objective is to create fully autonomous forklift trucks for paper reel handling. The result is expected to be of general benefit for industries that use forklift trucks in their material handling through higher operating efficiency and better flexibility with reduced risk for accidents and handling damages than if only manual forklift trucks are used. A brief overview of the state of the art in AGVs will be reported in order to better understand the new challenges and technologies. Among the emerging technologies used for vehicle automation, vision is one of the most promising in terms of versatility and efficiency, with a high potential to drastically reduce the costs.

1 - 14 of 14
CiteExportLink to result list
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