海上環境向けオープンボキャブラリ3Dシーングラフによる永続的メトリック・セマンティック記憶
Persistent Metric-Semantic Maritime Scene Memory from Open-Vocabulary 3D Scene Graphs
屋内・陸上向けのオープンボキャブラリ3Dシーングラフ手法を海上環境に適用し、エンティティ断片化を統合する後処理とVLM推論で永続的なシーン記憶を構築した論文。
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著者: Kim Alexander Christensen, Dina Zimmermann, Martin Steinert, Eleni Kelasidi
分類: cs.CV, cs.RO
原文アブストラクト
Three-dimensional scene graphs (3DSGs) can provide robots with persistent metric-semantic representations for spatial reasoning, but the existing methods have primarily been developed for indoor and terrestrial environments. In this paper we investigate how a representative outdoor open-vocabulary method transfers to maritime environments and which adaptations are needed. Our experiments show that zero-shot representative outdoor 3DSG descriptions agree with our labeled ground truth for a majority of geometry-supported objects on our data, but the resulting map exhibits considerable physical-entity fragmentation, and its terrestrial higher-level assumptions don't capture maritime spatial organization needs. We therefore keep the underlying mapping method and post-process its accumulated evidence. We show a metric-semantic consolidation method followed by geometry-grounded multi-view vision-language model (VLM) reasoning that reconciles remaining fragments into physical entities and groups entities through observations into local places. In parallel, we construct maritime macro regions using map priors and onboard water and structure observations, and associate them with the local places. The resulting representation reduces physical-entity fragmentation beyond both the baseline and our geometry-semantic consolidation component alone, supports correspondence of fixed local places across repeated maritime traversals, and improves contextual retrieval on our reviewed queries over tested baselines. These results show how an outdoor open-vocabulary map can be adapted into persistent maritime scene memory with our methods for object/place identity and spatial abstractions.