  In many applications, maintaining a consistent dense map of the environment
is key to enabling robotic platforms to perform higher level decision making.
Several works have addressed the challenge of creating precise dense 3D maps
from visual sensors providing depth information. However, during operation over
longer missions, reconstructions can easily become inconsistent due to
accumulated camera tracking error and delayed loop closure. Without explicitly
addressing the problem of map consistency, recovery from such distortions tends
to be difficult. We present a novel system for dense 3D mapping which addresses
the challenge of building consistent maps while dealing with scalability.
Central to our approach is the representation of the environment as a
collection of overlapping TSDF subvolumes. These subvolumes are localized
through feature-based camera tracking and bundle adjustment. Our main
contribution is a pipeline for identifying stable regions in the map, and to
fuse the contributing subvolumes. This approach allows us to reduce map growth
while still maintaining consistency. We demonstrate the proposed system on a
publicly available dataset and simulation engine, and demonstrate the efficacy
of the proposed approach for building consistent and scalable maps. Finally we
demonstrate our approach running in real-time on-board a lightweight MAV.
