  We propose a visual SLAM method by predicting and updating line flows that
represent sequential 2D projections of 3D line segments. While feature-based
SLAM methods have achieved excellent results, they still face problems in
challenging scenes containing occlusions, blurred images, and repetitive
textures. To address these problems, we leverage a line flow to encode the
coherence of line segment observations of the same 3D line along the temporal
dimension, which has been neglected in prior SLAM systems. Thanks to this line
flow representation, line segments in a new frame can be predicted according to
their corresponding 3D lines and their predecessors along the temporal
dimension. We create, update, merge, and discard line flows on-the-fly. We
model the proposed line flow based SLAM (LF-SLAM) using a Bayesian network.
Extensive experimental results demonstrate that the proposed LF-SLAM method
achieves state-of-the-art results due to the utilization of line flows.
Specifically, LF-SLAM obtains good localization and mapping results in
challenging scenes with occlusions, blurred images, and repetitive textures.
