SG2Loc: 3Dシーングラフを用いた逐次視覚位置推定
SG2Loc: Sequential Visual Localization on 3D Scene Graphs
3Dシーングラフを環境表現に用いた軽量な逐次視覚位置推定手法を提案。パーティクルフィルタとセマンティック特徴マッチングにより、大規模な画像データベースや点群を必要とせずに位置推定を実現する。
詳しい要約
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著者: Nicole Damblon, Olga Vysotska, Federico Tombari, Marc Pollefeys, Daniel Barath
分類: cs.CV
原文アブストラクト
Visual localization in complex indoor environments remains a critical challenge for robotics and AR applications. Sequential localization, where pose estimates are refined over time, is important for autonomous agents. However, traditional methods often require storing extensive image databases or point clouds, leading to significant overhead. This paper introduces a novel, lightweight approach to sequential visual localization using 3D scene graphs. Our method represents the environment with a compact scene graph, where nodes represent objects (with coarse meshes) and edges encode spatial relationships. For each image in the localization phase, we extract per-patch semantic features, predicting object identities. Localization is performed within a particle filter framework. Each particle, representing a camera pose, projects the coarse object meshes from the scene graph into the image, assigning object identities to patches based on visibility. The similarity of the per-patch features, in the input image, and object features from the scene graph determines the weight of a particle. Subsequent images are incorporated sequentially, refining the pose estimate. By leveraging a compact scene graph and efficient semantic matching, our method significantly reduces storage while maintaining performance on real-world datasets. The code will be available at https://github.com/DmblnNicole/sg2loc.
