SEM-GAT: explainable semantic pose estimation using learned graph attention
This paper proposes a Graph Neural Network (GNN)-based method for exploiting semantics and local geometry to guide the identification of reliable pointcloud registration candidates. Semantic and morphological features of the environment serve as key reference points for registration, enabling accura...
| 主要な著者: | , , , , |
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| フォーマット: | Conference item |
| 言語: | English |
| 出版事項: |
IEEE
2024
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