  Constructing a high-quality dense map in real-time is essential for robotics,
AR/VR, and digital twins applications. As Neural Radiance Field (NeRF) greatly
improves the mapping performance, in this paper, we propose a NeRF-based
mapping method that enables higher-quality reconstruction and real-time
capability even on edge computers. Specifically, we propose a novel
hierarchical hybrid representation that leverages implicit multiresolution hash
encoding aided by explicit octree SDF priors, describing the scene at different
levels of detail. This representation allows for fast scene geometry
initialization and makes scene geometry easier to learn. Besides, we present a
coverage-maximizing keyframe selection strategy to address the forgetting issue
and enhance mapping quality, particularly in marginal areas. To the best of our
knowledge, our method is the first to achieve high-quality NeRF-based mapping
on edge computers of handheld devices and quadrotors in real-time. Experiments
demonstrate that our method outperforms existing NeRF-based mapping methods in
geometry accuracy, texture realism, and time consumption. The code will be
released at: https://github.com/SYSU-STAR/H2-Mapping
