大規模環境における能動的ニューラルマッピング
Active Neural Mapping at Scale
NeRFベースの地図から一般化ボロノイグラフを抽出し、不確実性を考慮しながら大規模屋内環境を効率的に探索する能動的マッピングシステムを提案した。
著者: Zijia Kuang, Zike Yan, Hao Zhao, Guyue Zhou, Hongbin Zha
分類: cs.CV, cs.RO
原文アブストラクト
We introduce a NeRF-based active mapping system that enables efficient and robust exploration of large-scale indoor environments. The key to our approach is the extraction of a generalized Voronoi graph (GVG) from the continually updated neural map, leading to the synergistic integration of scene geometry, appearance, topology, and uncertainty. Anchoring uncertain areas induced by the neural map to the vertices of GVG allows the exploration to undergo adaptive granularity along a safe path that traverses unknown areas efficiently. Harnessing a modern hybrid NeRF representation, the proposed system achieves competitive results in terms of reconstruction accuracy, coverage completeness, and exploration efficiency even when scaling up to large indoor environments. Extensive results at different scales validate the efficacy of the proposed system.
