Planar Homography based Monocular SLAM Initialization Method

Simultaneous Localization and Mapping (SLAM) is a popular topic in autonomous robots navigation. It has been studied for decades in both computer vision and robotics communities. Monocular systems is more cost effective compared to RGBD or Stereo systems; however, it is relatively complicated to initialize due to scale uncertainty. Under certain conditions, it is assumed that the camera only moves in a planar scene, which provides us with homography constraints. In this paper, the efficiency of monocular initialization was improved based on the open source platform ORB-SLAM2, employing the algorithm based on planar homography constraints. We compared the improved algorithm with the source code of ORB-SLAM2 on the public datasets. It showed that our algorithm has better stability and robustness in the planar scene dataset and more initializing map points.