DynamicGlue: Epipolar and Time-Informed Data Association in Dynamic Environments using Graph Neural Networks
arxiv(2024)
摘要
The assumption of a static environment is common in many geometric computer
vision tasks like SLAM but limits their applicability in highly dynamic scenes.
Since these tasks rely on identifying point correspondences between input
images within the static part of the environment, we propose a graph neural
network-based sparse feature matching network designed to perform robust
matching under challenging conditions while excluding keypoints on moving
objects. We employ a similar scheme of attentional aggregation over graph edges
to enhance keypoint representations as state-of-the-art feature-matching
networks but augment the graph with epipolar and temporal information and
vastly reduce the number of graph edges. Furthermore, we introduce a
self-supervised training scheme to extract pseudo labels for image pairs in
dynamic environments from exclusively unprocessed visual-inertial data. A
series of experiments show the superior performance of our network as it
excludes keypoints on moving objects compared to state-of-the-art feature
matching networks while still achieving similar results regarding conventional
matching metrics. When integrated into a SLAM system, our network significantly
improves performance, especially in highly dynamic scenes.
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