Title: Semi-Semantic Line-Cluster Assisted Monocular SLAM for Indoor Environments,
Abstract: This paper presents a novel method to reduce the scale drift for indoor
monocular simultaneous localization and mapping (SLAM). We leverage the prior
knowledge that in the indoor environment, the line segments form tight
clusters, e.g. many door frames in a straight corridor are of the same shape,
size and orientation, so the same edges of these door frames form a tight line
segment cluster. We implement our method in the popular ORB-SLAM2, which also
serves as our baseline. In the front end we detect the line segments in each
frame and incrementally cluster them in the 3D space. In the back end, we
optimize the map imposing the constraint that the line segments of the same
cluster should be the same. Experimental results show that our proposed method
successfully reduces the scale drift for indoor monocular SLAM.