Patrick Kesper

Group(s): Neural Control and Robotics
Email:
pkesper@gwdg.de

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    Journal / Proceedings / Book
    Kesper, P. and Grinke, E. and Hesse, F. and Wörgötter, F. and Manoonpong, P. (2013).
    Obstacle/Gap Detection and Terrain Classification of Walking Robots based on a 2D Laser Range Finder. 16th International Conference on Climbing and Walking Robots and the Support Technologies for Mobile Machines CLAWAR, 419-426, 16. DOI: 10.1142/9789814525534_0053.
    BibTeX:
    @inproceedings{kespergrinkehesse2013,
      author = {Kesper, P. and Grinke, E. and Hesse, F. and Wörgötter, F. and Manoonpong, P.},
      title = {Obstacle/Gap Detection and Terrain Classification of Walking Robots based on a 2D Laser Range Finder},
      pages = {419-426},
      booktitle = {16th International Conference on Climbing and Walking Robots and the Support Technologies for Mobile Machines CLAWAR},
      year = {2013},
      number = {16},
      location = {Sidney (Australia)},
      month = {July 14-17},
      url = {https://www.worldscientific.com/doi/abs/10.1142/9789814525534_0053},
      doi = {10.1142/9789814525534_0053},
      abstract = {This paper utilizes a 2D laser range finder (LRF) to determine the behavior of a walking robot. The LRF provides information for 1) obstacle/gap detection as well as 2) terrain classification. The obstacle/gap detection is based on an edge detection with increased robustness and accuracy due to customized pre and post processing. Its output is used to drive obstacle/gap avoidance behavior or climbing behavior, depending on the height of obstacles or the depth of gaps. The terrain classification employs terrain roughness to select a proper gait with respect to the current terrain. As a result, the combination of these methods enables the robot to decide if obstacles and gaps can be climbed up/down or have to be avoided while at the same time a terrain specific gait can be chosen.}}
    Abstract: This paper utilizes a 2D laser range finder (LRF) to determine the behavior of a walking robot. The LRF provides information for 1) obstacle/gap detection as well as 2) terrain classification. The obstacle/gap detection is based on an edge detection with increased robustness and accuracy due to customized pre and post processing. Its output is used to drive obstacle/gap avoidance behavior or climbing behavior, depending on the height of obstacles or the depth of gaps. The terrain classification employs terrain roughness to select a proper gait with respect to the current terrain. As a result, the combination of these methods enables the robot to decide if obstacles and gaps can be climbed up/down or have to be avoided while at the same time a terrain specific gait can be chosen.
    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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