  It is well known that visual SLAM systems based on dense matching are locally
accurate but are also susceptible to long-term drift and map corruption. In
contrast, feature matching methods can achieve greater long-term consistency
but can suffer from inaccurate local pose estimation when feature information
is sparse. Based on these observations, we propose an RGB-D SLAM system that
leverages the advantages of both approaches: using dense frame-to-model
odometry to build accurate sub-maps and on-the-fly feature-based matching
across sub-maps for global map optimisation. In addition, we incorporate a
learning-based loop closure component based on 3-D features which further
stabilises map building. We have evaluated the approach on indoor sequences
from public datasets, and the results show that it performs on par or better
than state-of-the-art systems in terms of map reconstruction quality and pose
estimation. The approach can also scale to large scenes where other systems
often fail.
