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  • 1.
    Krug, Robert
    et al.
    Örebro University, School of Science and Technology.
    Stoyanov, Todor
    Örebro University, School of Science and Technology.
    Tincani, Vinicio
    Interdepart. Research Center “E. Piaggio”; University of Pisa, Pisa, Italy.
    Andreasson, Henrik
    Örebro University, School of Science and Technology.
    Mosberger, Rafael
    Örebro University, School of Science and Technology.
    Fantoni, Gualtiero
    Interdepart. Research Center “E. Piaggio”; University of Pisa, Pisa, Italy.
    Bicchi, Antonio
    Interdepart. Research Center “E. Piaggio”; University of Pisa, Pisa, Italy.
    Lilienthal, Achim
    Örebro University, School of Science and Technology.
    On Using Optimization-based Control instead of Path-Planning for Robot Grasp Motion Generation2015In: IEEE International Conference on Robotics and Automation (ICRA) - Workshop on Robotic Hands, Grasping, and Manipulation, 2015Conference paper (Refereed)
    Download full text (pdf)
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  • 2.
    Krug, Robert
    et al.
    Örebro University, School of Science and Technology.
    Stoyanov, Todor
    Örebro University, School of Science and Technology.
    Tincani, Vinicio
    University of Pisa, Pisa, Italy.
    Andreasson, Henrik
    Örebro University, School of Science and Technology.
    Mosberger, Rafael
    Örebro University, School of Science and Technology.
    Fantoni, Gualtiero
    University of Pisa, Pisa, Italy.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology.
    The Next Step in Robot Commissioning: Autonomous Picking and Palletizing2016In: IEEE Robotics and Automation Letters, E-ISSN 2377-3766, Vol. 1, no 1, p. 546-553Article in journal (Refereed)
    Abstract [en]

    So far, autonomous order picking (commissioning) systems have not been able to meet the stringent demands regarding speed, safety, and accuracy of real-world warehouse automation, resulting in reliance on human workers. In this letter, we target the next step in autonomous robot commissioning: automatizing the currently manual order picking procedure. To this end, we investigate the use case of autonomous picking and palletizing with a dedicated research platform and discuss lessons learned during testing in simplified warehouse settings. The main theoretical contribution is a novel grasp representation scheme which allows for redundancy in the gripper pose placement. This redundancy is exploited by a local, prioritized kinematic controller which generates reactive manipulator motions on-the-fly. We validated our grasping approach by means of a large set of experiments, which yielded an average grasp acquisition time of 23.5 s at a success rate of 94.7%. Our system is able to autonomously carry out simple order picking tasks in a humansafe manner, and as such serves as an initial step toward future commercial-scale in-house logistics automation solutions.

    Download full text (pdf)
    fulltext
  • 3.
    Mosberger, Rafael
    Örebro University, School of Science and Technology.
    Vision-based Human Detection from Mobile Machinery in Industrial Environments2016Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    The problem addressed in this thesis is the detection, localisation and tracking of human workers from mobile industrial machinery using a customised vision system developed at Örebro University. Coined the RefleX Vision System, its hardware configuration and computer vision algorithms were specifically designed for real-world industrial scenarios where workers are required to wear protective high-visibility garments with retro-reflective markers. The demand for robust industry-purpose human sensing methods originates from the fact that many industrial environments represent work spaces that are shared between humans and mobile machinery. Typical examples of such environments include construction sites, surface and underground mines, storage yards and warehouses. Here, accidents involving mobile equipment and human workers frequently result in serious injuries and fatalities. Robust sensor-based detection of humans in the surrounding of mobile equipment is therefore an active research topic and represents a crucial requirement for safe vehicle operation and accident prevention in increasingly automated production sites. Addressing the described safety issue, this thesis presents a collection of papers which introduce, analyse and evaluate a novel vision-based method for detecting humans equipped with protective high-visibility garments in the neighbourhood of manned or unmanned industrial vehicles. The thesis provides a comprehensive discussion of the numerous aspects regarding the design of the hardware and the computer vision algorithms that constitute the vision system. An active nearinfrared camera setup that is customised for the robust perception of retroreflective markers builds the basis for the sensing method. Using its specific input, a set of computer vision and machine learning algorithms then perform extraction, analysis, classification and localisation of the observed reflective patterns, and eventually detection and tracking of workers with protective garments. Multiple real-world challenges, which existing methods frequently struggle to cope with, are discussed throughout the thesis, including varying ambient lighting conditions and human body pose variation. The presented work has been carried out with a strong focus on industrial applicability, and therefore includes an extensive experimental evaluation in a number of different real-world indoor and outdoor work environments.

    List of papers
    1. Estimating the 3D Position of Humans Wearing a Reflective Vest Using a Single Camera System
    Open this publication in new window or tab >>Estimating the 3D Position of Humans Wearing a Reflective Vest Using a Single Camera System
    2014 (English)In: Field and Service Robotics: Results of the 8th International Conference / [ed] Yoshida, Kazuya, Tadokoro, Satoshi, Springer Berlin/Heidelberg, 2014, p. 143-157Chapter in book (Refereed)
    Abstract [en]

    This chapter presents a novel possible solution for people detection and estimation of their 3D position in challenging shared environments. Addressing safety critical applications in industrial environments, we make the basic assumption that people wear reflective vests. In order to detect these vests and to discriminate them from other reflective material, we propose an approach based on a single camera equipped with an IR flash. The camera acquires pairs of images, one with and one without IR flash, in short succession. The images forming a pair are then related to each other through feature tracking, which allows to discard features for which the relative intensity difference is small and which are thus not believed to belong to a reflective vest. Next, the local neighbourhood of the remaining features is further analysed. First, a Random Forest classifier is used to discriminate between features caused by a reflective vest and features caused by some other reflective materials. Second, the distance between the camera and the vest features is estimated using a Random Forest regressor. The proposed system was evaluated in one indoor and two challenging outdoor scenarios. Our results indicate very good classification performance and remarkably accurate distance estimation especially in combination with the SURF descriptor, even under direct exposure to sunlight.

    Place, publisher, year, edition, pages
    Springer Berlin/Heidelberg, 2014
    Series
    Springer Tracts in Advanced Robotics, ISSN 1610-7438 ; 92
    Keywords
    People Detection, Industrial Safety, Reflective Vest Detection
    National Category
    Computer Sciences
    Research subject
    Computer Science
    Identifiers
    urn:nbn:se:oru:diva-41334 (URN)10.1007/978-3-642-40686-7_10 (DOI)2-s2.0-84897710550 (Scopus ID)978-3-642-40685-0 (ISBN)978-3-642-40686-7 (ISBN)
    Projects
    SAVIE
    Available from: 2015-01-14 Created: 2015-01-14 Last updated: 2020-04-28Bibliographically approved
    2. An Inexpensive Monocular Vision System for Tracking Humans in Industrial Environments
    Open this publication in new window or tab >>An Inexpensive Monocular Vision System for Tracking Humans in Industrial Environments
    2013 (English)In: Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), IEEE conference proceedings, 2013, p. 5850-5857Conference paper, Published paper (Refereed)
    Abstract [en]

    We report on a novel vision-based method for reliable human detection from vehicles operating in industrial environments in the vicinity of workers. By exploiting the fact that reflective vests represent a standard safety equipment on most industrial worksites, we use a single camera system and active IR illumination to detect humans by identifying the reflective vest markers. Adopting a sparse feature based approach, we classify vest markers against other reflective material and perform supervised learning of the object distance based on local image descriptors. The integration of the resulting per-feature 3D position estimates in a particle filter finally allows to perform human tracking in conditions ranging from broad daylight to complete darkness.

    Place, publisher, year, edition, pages
    IEEE conference proceedings, 2013
    Series
    Robotics and Automation (ICRA), 2013 IEEE International Conference on, ISSN 1050-4729
    Keywords
    Human Detection, Robot Vision, Industrial Safety
    National Category
    Computer Sciences
    Research subject
    Computer Science
    Identifiers
    urn:nbn:se:oru:diva-30767 (URN)10.1109/ICRA.2013.6631419 (DOI)000337617305131 ()2-s2.0-84887269624 (Scopus ID)978-1-4673-5641-1 (ISBN)
    Conference
    2013 IEEE International Conference on Robotics and Automation (ICRA) Karlsruhe, Germany, May 6-10, 2013
    Available from: 2013-09-11 Created: 2013-09-11 Last updated: 2018-01-11Bibliographically approved
    3. Multi-human Tracking using High-visibility Clothing for Industrial Safety
    Open this publication in new window or tab >>Multi-human Tracking using High-visibility Clothing for Industrial Safety
    2013 (English)In: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2013, p. 638-644Conference paper, Published paper (Refereed)
    Abstract [en]

    We propose and evaluate a system for detecting and tracking multiple humans wearing high-visibility clothing from vehicles operating in industrial work environments. We use a customized stereo camera setup equipped with IR flash and IR filter to detect the reflective material on the worker's garments and estimate their trajectories in 3D space. An evaluation in two distinct industrial environments with different degrees of complexity demonstrates the approach to be robust and accurate for tracking workers in arbitrary body poses, under occlusion, and under a wide range of different illumination settings.

    Series
    Intelligent Robots and Systems (IROS), 2013 IEEE/RSJ International Conference on, ISSN 2153-0858
    Keywords
    Human Detection, Robot Vision, Industrial Safety
    National Category
    Computer and Information Sciences
    Research subject
    Computer Science
    Identifiers
    urn:nbn:se:oru:diva-32962 (URN)10.1109/IROS.2013.6696418 (DOI)000331367400094 ()2-s2.0-84893714622 (Scopus ID)
    Conference
    2013 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) November 3-7, 2013. Tokyo, Japan
    Available from: 2014-01-08 Created: 2014-01-08 Last updated: 2018-01-11Bibliographically approved
    4. A customized vision system for tracking humans wearing reflective safety clothing from industrial vehicles and machinery
    Open this publication in new window or tab >>A customized vision system for tracking humans wearing reflective safety clothing from industrial vehicles and machinery
    2014 (English)In: Sensors, E-ISSN 1424-8220, Vol. 14, no 10, p. 17952-17980Article in journal (Refereed) Published
    Abstract [en]

    This article presents a novel approach for vision-based detection and tracking of humans wearing high-visibility clothing with retro-reflective markers. Addressing industrial applications where heavy vehicles operate in the vicinity of humans, we deploy a customized stereo camera setup with active illumination that allows for efficient detection of the reflective patterns created by the worker's safety garments. After segmenting reflective objects from the image background, the interest regions are described with local image feature descriptors and classified in order to discriminate safety garments from other reflective objects in the scene. In a final step, the trajectories of the detected humans are estimated in 3D space relative to the camera. We evaluate our tracking system in two industrial real-world work environments on several challenging video sequences. The experimental results indicate accurate tracking performance and good robustness towards partial occlusions, body pose variation, and a wide range of different illumination conditions.

    Keywords
    infrared vision, human detection, industrial safety, high-visibility clothing
    National Category
    Computer Vision and Robotics (Autonomous Systems)
    Research subject
    Computer Science
    Identifiers
    urn:nbn:se:oru:diva-39811 (URN)10.3390/s141017952 (DOI)000344455700006 ()25264956 (PubMedID)2-s2.0-84908518665 (Scopus ID)
    Available from: 2014-12-16 Created: 2014-12-16 Last updated: 2022-02-10Bibliographically approved
    Download full text (pdf)
    Introductory chapter
    Download (pdf)
    Cover
    Download (pdf)
    Spikblad
  • 4.
    Mosberger, Rafael
    et al.
    Örebro University, School of Science and Technology.
    Andreasson, Henrik
    Örebro University, School of Science and Technology.
    An Inexpensive Monocular Vision System for Tracking Humans in Industrial Environments2013In: Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), IEEE conference proceedings, 2013, p. 5850-5857Conference paper (Refereed)
    Abstract [en]

    We report on a novel vision-based method for reliable human detection from vehicles operating in industrial environments in the vicinity of workers. By exploiting the fact that reflective vests represent a standard safety equipment on most industrial worksites, we use a single camera system and active IR illumination to detect humans by identifying the reflective vest markers. Adopting a sparse feature based approach, we classify vest markers against other reflective material and perform supervised learning of the object distance based on local image descriptors. The integration of the resulting per-feature 3D position estimates in a particle filter finally allows to perform human tracking in conditions ranging from broad daylight to complete darkness.

  • 5.
    Mosberger, Rafael
    et al.
    Örebro University, School of Science and Technology.
    Andreasson, Henrik
    Örebro University, School of Science and Technology.
    Estimating the 3d position of humans wearing a reflective vest using a single camera system2012In: Proceedings of the International Conference on Field and Service Robotics (FSR), Springer, 2012Conference paper (Refereed)
    Abstract [en]

    This paper presents a novel possible solution for people detection and estimation of their 3D position in challenging shared environments. Addressing safety critical applications in industrial environments, we make the basic assumption that people wear reflective vests. In order to detect these vests and to discriminate them from other reflective material, we propose an approach based on a single camera equipped with an IR flash. The camera acquires pairs of images, one with and one without IR flash, in short succession. The images forming a pair are then related to each other through feature tracking, which allows to discard features for which the relative intensity difference is small and which are thus not believed to belong to a reflective vest. Next, the local neighbourhood of the remaining features is further analysed. First, a Random Forest classifier is used to discriminate between features caused by a reflective vest and features caused by some other reflective materials. Second, the distance between the camera and the vest features is estimated using a Random Forest regressor. The proposed system was evaluated in one indoor and two challenging outdoor scenarios. Our results indicate very good classification performance and remarkably accurate distance estimation especially in combination with the SURF descriptor, even under direct exposure to sunlight.

  • 6.
    Mosberger, Rafael
    et al.
    Örebro University, School of Science and Technology.
    Andreasson, Henrik
    Örebro University, School of Science and Technology.
    Estimating the 3D Position of Humans Wearing a Reflective Vest Using a Single Camera System2014In: Field and Service Robotics: Results of the 8th International Conference / [ed] Yoshida, Kazuya, Tadokoro, Satoshi, Springer Berlin/Heidelberg, 2014, p. 143-157Chapter in book (Refereed)
    Abstract [en]

    This chapter presents a novel possible solution for people detection and estimation of their 3D position in challenging shared environments. Addressing safety critical applications in industrial environments, we make the basic assumption that people wear reflective vests. In order to detect these vests and to discriminate them from other reflective material, we propose an approach based on a single camera equipped with an IR flash. The camera acquires pairs of images, one with and one without IR flash, in short succession. The images forming a pair are then related to each other through feature tracking, which allows to discard features for which the relative intensity difference is small and which are thus not believed to belong to a reflective vest. Next, the local neighbourhood of the remaining features is further analysed. First, a Random Forest classifier is used to discriminate between features caused by a reflective vest and features caused by some other reflective materials. Second, the distance between the camera and the vest features is estimated using a Random Forest regressor. The proposed system was evaluated in one indoor and two challenging outdoor scenarios. Our results indicate very good classification performance and remarkably accurate distance estimation especially in combination with the SURF descriptor, even under direct exposure to sunlight.

  • 7.
    Mosberger, Rafael
    et al.
    Örebro University, School of Science and Technology.
    Andreasson, Henrik
    Örebro University, School of Science and Technology.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology.
    A customized vision system for tracking humans wearing reflective safety clothing from industrial vehicles and machinery2014In: Sensors, E-ISSN 1424-8220, Vol. 14, no 10, p. 17952-17980Article in journal (Refereed)
    Abstract [en]

    This article presents a novel approach for vision-based detection and tracking of humans wearing high-visibility clothing with retro-reflective markers. Addressing industrial applications where heavy vehicles operate in the vicinity of humans, we deploy a customized stereo camera setup with active illumination that allows for efficient detection of the reflective patterns created by the worker's safety garments. After segmenting reflective objects from the image background, the interest regions are described with local image feature descriptors and classified in order to discriminate safety garments from other reflective objects in the scene. In a final step, the trajectories of the detected humans are estimated in 3D space relative to the camera. We evaluate our tracking system in two industrial real-world work environments on several challenging video sequences. The experimental results indicate accurate tracking performance and good robustness towards partial occlusions, body pose variation, and a wide range of different illumination conditions.

  • 8.
    Mosberger, Rafael
    et al.
    Örebro University, School of Science and Technology.
    Andreasson, Henrik
    Örebro University, School of Science and Technology.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology.
    Multi-human Tracking using High-visibility Clothing for Industrial Safety2013In: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2013, p. 638-644Conference paper (Refereed)
    Abstract [en]

    We propose and evaluate a system for detecting and tracking multiple humans wearing high-visibility clothing from vehicles operating in industrial work environments. We use a customized stereo camera setup equipped with IR flash and IR filter to detect the reflective material on the worker's garments and estimate their trajectories in 3D space. An evaluation in two distinct industrial environments with different degrees of complexity demonstrates the approach to be robust and accurate for tracking workers in arbitrary body poses, under occlusion, and under a wide range of different illumination settings.

  • 9.
    Mosberger, Rafael
    et al.
    Örebro University, School of Science and Technology.
    Leibe, Bastian
    Aachen University, Aachen, Germany.
    Andreasson, Henrik
    Örebro University, School of Science and Technology.
    Lilienthal, Achim
    Örebro University, School of Science and Technology.
    Multi-band Hough Forests for detecting humans with Reflective Safety Clothing from mobile machinery2015In: Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), IEEE Computer Society, 2015, p. 697-703Conference paper (Refereed)
    Abstract [en]

    We address the problem of human detection from heavy mobile machinery and robotic equipment operating at industrial working sites. Exploiting the fact that workers are typically obliged to wear high-visibility clothing with reflective markers, we propose a new recognition algorithm that specifically incorporates the highly discriminative features of the safety garments in the detection process. Termed Multi-band Hough Forest, our detector fuses the input from active near-infrared (NIR) and RGB color vision to learn a human appearance model that not only allows us to detect and localize industrial workers, but also to estimate their body orientation. We further propose an efficient pipeline for automated generation of training data with high-quality body part annotations that are used in training to increase detector performance. We report a thorough experimental evaluation on challenging image sequences from a real-world production environment, where persons appear in a variety of upright and non-upright body positions.

  • 10.
    Mosberger, Rafael
    et al.
    Örebro University, School of Science and Technology.
    Schaffernicht, Erik
    Andreasson, Henrik
    Örebro University, School of Science and Technology.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology.
    Inferring human body posture information from reflective patterns of protective work garments2016In: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Institute of Electrical and Electronics Engineers (IEEE), 2016, p. 4131-4136Conference paper (Refereed)
    Abstract [en]

    We address the problem of extracting human body posture labels, upper body orientation and the spatial location of individual body parts from near-infrared (NIR) images depicting patterns of retro-reflective markers. The analyzed patterns originate from the observation of humans equipped with protective high-visibility garments that represent common safety equipment in the industrial sector. Exploiting the shape of the observed reflectors we adopt shape matching based on the chamfer distance and infer one of seven discrete body posture labels as well as the approximate upper body orientation with respect to the camera. We then proceed to analyze the NIR images on a pixel scale and estimate a figure-ground segmentation together with human body part labels using classification of densely extracted local image patches. Our results indicate a body posture classification accuracy of 80% and figure-ground segmentations with 87% accuracy.

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