Lars Berscheid

Group(s): Neural Control and Robotics ,
Computer Vision
Email:
lars.berscheid@phys.uni-goettingen.de

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    Year
    Title
    Journal / Proceedings / Book
    Reich, S. and Seer, M. and Berscheid, L. and Wörgötter, F. and Braun, J. (2018).
    Omnidirectional visual odometry for flying robots using low-power hardware. Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP): Visapp, 499-507, 5. DOI: 10.5220/0006509704990507.
    BibTeX:
    @inproceedings{reichseerberscheid2018,
      author = {Reich, S. and Seer, M. and Berscheid, L. and Wörgötter, F. and Braun, J.},
      title = {Omnidirectional visual odometry for flying robots using low-power hardware},
      pages = {499-507},
      booktitle = {Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP): Visapp},
      year = {2018},
      volume= {5},
      location = {Funchal, Madeira (Portugal)},
      month = {January 27-29},
      organization = {INSTICC},
      publisher = {SciTePress},
      doi = {10.5220/0006509704990507},
      abstract = {Currently, flying robotic systems are in development for package delivery, aerial exploration in catastrophe areas, or maintenance tasks. While many flying robots are used in connection with powerful, stationary computing systems, the challenge in autonomous devices---especially in indoor-rescue or rural missions---lies in the need to do all processing internally on low power hardware. Furthermore, the device cannot rely on a well ordered or marked surrounding. These requirements make computer vision an important and challenging task for such systems. To cope with the cumulative problems of low frame rates in combination with high movement rates of the aerial device, a hyperbolic mirror is mounted on top of a quadrocopter, recording omnidirectional images, which can capture features during fast pose changes. The viability of this approach will be demonstrated by analysing several scenes. Here, we present a novel autonomous robot, which performs all computations online on low power embedded hardware and is therefore a truly autonomous robot. Furthermore, we introduce several novel algorithms, which have a low computational complexity and therefore enable us to refrain from external resources.}}
    Abstract: Currently, flying robotic systems are in development for package delivery, aerial exploration in catastrophe areas, or maintenance tasks. While many flying robots are used in connection with powerful, stationary computing systems, the challenge in autonomous devices---especially in indoor-rescue or rural missions---lies in the need to do all processing internally on low power hardware. Furthermore, the device cannot rely on a well ordered or marked surrounding. These requirements make computer vision an important and challenging task for such systems. To cope with the cumulative problems of low frame rates in combination with high movement rates of the aerial device, a hyperbolic mirror is mounted on top of a quadrocopter, recording omnidirectional images, which can capture features during fast pose changes. The viability of this approach will be demonstrated by analysing several scenes. Here, we present a novel autonomous robot, which performs all computations online on low power embedded hardware and is therefore a truly autonomous robot. Furthermore, we introduce several novel algorithms, which have a low computational complexity and therefore enable us to refrain from external resources.
    Review:
    Kesper, P. and Berscheid, L. and Wörgötter, F. and Manoonpong, P. (2015).
    A Generic Approach to Self-localization and Mapping of Mobile Robots Without Using a Kinematic Model. Towards Autonomous Robotic Systems, 136--142. DOI: 10.1007/978-3-319-22416-9_15.
    BibTeX:
    @inproceedings{kesperberscheidwoergoetter2015,
      author = {Kesper, P. and Berscheid, L. and Wörgötter, F. and Manoonpong, P.},
      title = {A Generic Approach to Self-localization and Mapping of Mobile Robots Without Using a Kinematic Model},
      pages = {136--142},
      booktitle = {Towards Autonomous Robotic Systems},
      year = {2015},
      editor = {Dixon, Clare and Tuyls, Karl},
      publisher = {Springer International Publishing},
      url = {https://link.springer.com/chapter/10.1007/978-3-319-22416-9_15},
      doi = {10.1007/978-3-319-22416-9_15},
      abstract = {In this paper a generic approach to the SLAM (Simultaneous Localization and Mapping) problem is proposed. The approach is based on a probabilistic SLAM algorithm and employs only two portable sensors, an inertial measurement unit (IMU) and a laser range finder (LRF) to estimate the state and environment of a robot. Scan-matching is applied to compensate for noisy IMU measurements. This approach does not require any robot-specific characteristics, e.g. wheel encoders or kinematic models. In principle, this minimal sensory setup can be mounted on different robot systems without major modifications to the underlying algorithms. The sensory setup with the probabilistic algorithm is tested in real-world experiments on two different kinds of robots: a simple two-wheeled robot and the six-legged hexapod AMOSII. The obtained results indicate a successful implementation of the approach and confirm its generic nature. On both robots, the SLAM problem can be solved with reasonable accuracy.}}
    Abstract: In this paper a generic approach to the SLAM (Simultaneous Localization and Mapping) problem is proposed. The approach is based on a probabilistic SLAM algorithm and employs only two portable sensors, an inertial measurement unit (IMU) and a laser range finder (LRF) to estimate the state and environment of a robot. Scan-matching is applied to compensate for noisy IMU measurements. This approach does not require any robot-specific characteristics, e.g. wheel encoders or kinematic models. In principle, this minimal sensory setup can be mounted on different robot systems without major modifications to the underlying algorithms. The sensory setup with the probabilistic algorithm is tested in real-world experiments on two different kinds of robots: a simple two-wheeled robot and the six-legged hexapod AMOSII. The obtained results indicate a successful implementation of the approach and confirm its generic nature. On both robots, the SLAM problem can be solved with reasonable accuracy.
    Review:

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