  Dense SLAM based on monocular cameras does indeed have immense application
value in the field of AR/VR, especially when it is performed on a mobile
device. In this paper, we propose a novel method that integrates a light-weight
depth completion network into a sparse SLAM system using a multi-basis depth
representation, so that dense mapping can be performed online even on a mobile
phone. Specifically, we present a specifically optimized multi-basis depth
completion network, called BBC-Net, tailored to the characteristics of
traditional sparse SLAM systems. BBC-Net can predict multiple balanced bases
and a confidence map from a monocular image with sparse points generated by
off-the-shelf keypoint-based SLAM systems. The final depth is a linear
combination of predicted depth bases that can be optimized by tuning the
corresponding weights. To seamlessly incorporate the weights into traditional
SLAM optimization and ensure efficiency and robustness, we design a set of
depth weight factors, which makes our network a versatile plug-in module,
facilitating easy integration into various existing sparse SLAM systems and
significantly enhancing global depth consistency through bundle adjustment. To
verify the portability of our method, we integrate BBC-Net into two
representative SLAM systems. The experimental results on various datasets show
that the proposed method achieves better performance in monocular dense mapping
than the state-of-the-art methods. We provide an online demo running on a
mobile phone, which verifies the efficiency and mapping quality of the proposed
method in real-world scenarios.
