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| blog:cram_icra_2022 [2022/06/01 10:18] – [CRAM @ ICRA 2022] gkazhoya | blog:cram_icra_2022 [2022/06/01 10:29] (current) – gkazhoya |
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| | ====== CRAM @ ICRA 2022 ====== |
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| {{ :blog:prior4pe-picknplacedishwasherv3.mp4|}} | <html><div style="float:right; margin-left:10px;"> |
| | <video width="448" height="252" controls> |
| ====== CRAM @ ICRA 2022 ====== | <source src="http://cram-system.org/_media/blog/prior4pe-picknplacedishwasherv3.mp4" type="video/mp4"> |
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| Our paper titled "Improving object pose estimation by fusion with a multimodal prior -- utilizing uncertainty-based CNN pipelines for robotics" that is published at [[https://ieeexplore.ieee.org/document/9670642|RAL]] has been presented at ICRA 2022 last week. The paper presents an approach to estimate 3D object poses by combining deep learning approaches with prior knowledge about the object. CRAM was used in the paper for applying the perception system on a physical robot and to evaluate the accuracy of perception by using it for mobile pick and place tasks. | Our paper titled "Improving object pose estimation by fusion with a multimodal prior -- utilizing uncertainty-based CNN pipelines for robotics" that is published at [[https://ieeexplore.ieee.org/document/9670642|RAL]] has been presented at ICRA 2022 last week. The paper presents an approach to estimate 3D object poses by combining deep learning approaches with prior knowledge about the object. CRAM was used in the paper to apply the perception system on a physical robot and to evaluate the accuracy of perception by using it for mobile pick and place tasks. |
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