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Hybrid ICP

arXiv 2109.07559 ↗First posted 15 Sep 2021cs.CV cs.ROcited by 0

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Abstract (arXiv)

ICP algorithms typically involve a fixed choice of data association method and a fixed choice of error metric. In this paper, we propose Hybrid ICP, a novel and flexible ICP variant which dynamically optimises both the data association method and error metric based on the live image of an object and the current ICP estimate. We show that when used for object pose estimation, Hybrid ICP is more accurate and more robust to noise than other commonly used ICP variants. We also consider the setting where ICP is applied sequentially with a moving camera, and we study the trade-off between the accuracy of each ICP estimate and the number of ICP estimates available within a fixed amount of time.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 7 of its 21 references that are in the map. First posted on arXiv 15 Sep 2021.

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7 of 21 references.

References · 7

  1. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
  2. 2015ShapeNet: An Information-Rich 3D Model Repositorycited by 2,897
  3. 2020CosyPose: Consistent multi-view multi-object 6D pose estimationcited by 287
  4. 2019Pix2Pose: Pixel-Wise Coordinate Regression of Objects for 6D Pose Estimationcited by 218
  5. 2020BOP Challenge 2020 on 6D Object Localizationcited by 193
  6. 2021Coarse-to-Fine Imitation Learning: Robot Manipulation from a Single Demonstrationcited by 93
  7. 2021Coarse-to-Fine for Sim-to-Real: Sub-Millimetre Precision Across Wide Task Spacescited by 9

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