We propose a method of visual SLAM by predicting and updating line flows that represent sequential 2D projections of 3D line segments. While indirect SLAM methods using points and line segments have achieved excellent results, they still face problems in challenging scenarios such as occlusions, image blur , and repetitive textures. To deal with these problems, we leverage line flows which encode the coherence of 2D and 3D line segments in spatial and temporal domains as the sequence of all the 2D line segments corresponding to a specific 3D line segment . Thanks to the line flow representation, the corresponding 2D line segment in a new frame can be predicted based on 2D and 3D line segment motions . We create, update, merge, and discard line flows on-the-fly. We model our Line Flow-based SLAM (LF-SLAM) using a Bayesian network. We perform short-term optimization in front-end, and long-term optimization in back-end. The constraints introduced in line flows improve the performance of our LF-SLAM. Extensive experimental results demonstrate that our method achieves better performance than state-of-the-art direct and indirect SLAM approaches . Specifically, it obtains good localization and mapping results in challenging scenes with occlusions, image blur , and repetitive textures.
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.
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method of visual SLAM
visual SLAM method
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indirect SLAM methods using points and line segments
feature-based SLAM methods
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scenarios such as occlusions, image blur
scenes containing occlusions, blurred images
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deal with
address
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line flows which
a line flow to
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2D and 3D line segments in spatial and temporal domains as the sequence of all the 2D line segments corresponding to a specific
line segment observations of the same
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segment
along the temporal dimension, which has been neglected in prior SLAM systems
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the
this
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the corresponding 2D line segment
line segments
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based on 2D and
according to their corresponding
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line segment motions
lines and their predecessors along the temporal dimension
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our Line Flow-based
the proposed line flow based
D
We perform short-term optimization in front-end, and long-term optimization in back-end. The constraints introduced in line flows improve the performance of our LF-SLAM.
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our method achieves better performance than
the proposed LF-SLAM method achieves
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direct and indirect SLAM approaches
results due to the utilization of line flows
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it
LF-SLAM
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image blur
blurred images