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Depth Map Based Pose Estimation

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Bin-Picking

Part of the book series: Studies in Systems, Decision and Control ((SSDC,volume 44))

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Abstract

In this chapter, the 6D pose estimation problem is solved using depth maps. Depth maps contain 3D scanner data but saved in a 2D image. This allows for very efficient scene analysis. An approach is described how grasp poses on unknown objects can be computed in real-time allowing for near time optimal grasping. It is further described how the approach can be extended for bin-picking tasks of known objects by combining the visual scene analysis with inertial feature analysis. Experiments close the chapter.

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Notes

  1. 1.

    Precise object poses are needed, in cases where objects are only allowed to be grasped at predefined regions.

  2. 2.

    Not considering ambiguities arising from symmetry.

  3. 3.

    Note that the index \(^S\!\) is omitted in Eqs. 4.9 and 4.10.

  4. 4.

    Grasps in which the gripper is closed to grasp are called outer grasps, grasps in which the gripper is opened to grasp are called inner grasps.

  5. 5.

    Inertia Measurement Unit.

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Correspondence to Dirk Buchholz .

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© 2016 Springer International Publishing Switzerland

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Buchholz, D. (2016). Depth Map Based Pose Estimation. In: Bin-Picking. Studies in Systems, Decision and Control, vol 44. Springer, Cham. https://doi.org/10.1007/978-3-319-26500-1_4

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  • DOI: https://doi.org/10.1007/978-3-319-26500-1_4

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-26498-1

  • Online ISBN: 978-3-319-26500-1

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