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

VIP-SLAM: An Efficient Tightly-Coupled RGB-D Visual Inertial Planar SLAM