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- W4386876300 abstract "The most commonly used method for addressing 3D geometric registration is the iterative closet-point algorithm, this approach is incremental and prone to drift over multiple consecutive frames. The Common strategy to address the drift is the pose graph optimization subsequent to frame-to-frame registration, incorporating a loop closure process that identifies previously visited places. In this paper, we explore a framework that replaces traditional geometric registration and pose graph optimization with a learned model utilizing hierarchical attention mechanisms and graph neural networks. We propose a strategy to condense the data flow, preserving essential information required for the precise estimation of rigid poses. Our results, derived from tests on the KITTI Odometry dataset, demonstrate a significant improvement in pose estimation accuracy. This improvement is especially notable in determining rotational components when compared with results obtained through conventional multi-way registration via pose graph optimization. The code will be made available upon completion of the review process." @default.
- W4386876300 created "2023-09-20" @default.
- W4386876300 creator A5030200153 @default.
- W4386876300 creator A5049722189 @default.
- W4386876300 date "2023-09-18" @default.
- W4386876300 modified "2023-09-27" @default.
- W4386876300 title "Hierarchical Attention and Graph Neural Networks: Toward Drift-Free Pose Estimation" @default.
- W4386876300 doi "https://doi.org/10.48550/arxiv.2309.09934" @default.
- W4386876300 hasPublicationYear "2023" @default.
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