4.7 Article

Line Flow Based Simultaneous Localization and Mapping

期刊

IEEE TRANSACTIONS ON ROBOTICS
卷 37, 期 5, 页码 1416-1432

出版社

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TRO.2021.3061403

关键词

Simultaneous localization and mapping; Image segmentation; Cameras; Motion segmentation; Image reconstruction; Optimization; Feature extraction; Line segment extraction and matching; simultaneous localization and mapping (SLAM); structure from motion (SfM)

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资金

  1. National Key Research and Development Program of China [2017YFB1002601]
  2. National Natural Science Foundation of China [61632003, 61771026]

向作者/读者索取更多资源

The article proposes a visual SLAM method based on line flows to predict and update 2-D projections of 3-D line segments in challenging scenes. Through Bayesian network modeling, LF-SLAM effectively addresses problems like occlusions, blurred images, and repetitive textures, achieving state-of-the-art localization and mapping results in complex environments.
In this article, we propose a visual simultaneous localization and mapping (SLAM) method by predicting and updating line flows that represent sequential 2-D projections of 3-D 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 3-D 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 3-D 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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