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.

翻译：我们提出了一种基于神经辐射场（NeRF）的主动建图系统，能够高效且鲁棒地探索大规模室内环境。本方法的关键在于从持续更新的神经地图中提取广义沃罗诺伊图（GVG），从而实现场景几何、外观、拓扑与不确定性的协同整合。将神经地图所引发的不确定区域锚定至GVG的顶点，可使探索过程沿一条安全路径以自适应粒度高效穿越未知区域。通过利用一种现代混合NeRF表示，所提出的系统在重建精度、覆盖完整性与探索效率方面均取得了具有竞争力的结果，即使扩展至大规模室内环境时亦然。不同规模场景下的广泛实验结果验证了该系统的有效性。