  In this paper, we propose a tightly-coupled SLAM system fused with RGB,
Depth, IMU and structured plane information. Traditional sparse points based
SLAM systems always maintain a mass of map points to model the environment.
Huge number of map points bring us a high computational complexity, making it
difficult to be deployed on mobile devices. On the other hand, planes are
common structures in man-made environment especially in indoor environments. We
usually can use a small number of planes to represent a large scene. So the
main purpose of this article is to decrease the high complexity of sparse
points based SLAM. We build a lightweight back-end map which consists of a few
planes and map points to achieve efficient bundle adjustment (BA) with an equal
or better accuracy. We use homography constraints to eliminate the parameters
of numerous plane points in the optimization and reduce the complexity of BA.
We separate the parameters and measurements in homography and point-to-plane
constraints and compress the measurements part to further effectively improve
the speed of BA. We also integrate the plane information into the whole system
to realize robust planar feature extraction, data association, and global
consistent planar reconstruction. Finally, we perform an ablation study and
compare our method with similar methods in simulation and real environment
data. Our system achieves obvious advantages in accuracy and efficiency. Even
if the plane parameters are involved in the optimization, we effectively
simplify the back-end map by using planar structures. The global bundle
adjustment is nearly 2 times faster than the sparse points based SLAM
algorithm.
