3D Semantic VSLAM of Dynamic Environment Based on YOLACT

A Visual Simultaneous Localization and Mapping (VSLAM) method is proposed to construct a 3D environment map in the creation of dynamic noise information which leads to significant errors in camera pose estimation and a substantial number of noise points. For this problem, this paper proposed a metho...

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Bibliographic Details
Published in:Mathematical problems in engineering 2022-09, Vol.2022, p.1-12
Main Authors: Wu, Dongyan, Xie, Bingbo, Tao, Chongben
Format: Article
Language:English
Subjects:
Ablation
Algorithms
Brain research
Cameras
Deep learning
Feature extraction
Flow mapping
Image segmentation
Localization
Mapping
Methods
Neural networks
Optical flow (image analysis)
Pose estimation
Robots
Semantic segmentation
Semantics
Simultaneous localization and mapping
Trajectories
Citations: Items that this one cites
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Summary:A Visual Simultaneous Localization and Mapping (VSLAM) method is proposed to construct a 3D environment map in the creation of dynamic noise information which leads to significant errors in camera pose estimation and a substantial number of noise points. For this problem, this paper proposed a method based on YOLACT, which combined optical flow and ViBe+ semantic map construction algorithm. First, our approach uses LK optical flow method to estimate the overall motion trajectory of adjacent frames. Then the trajectory data are employed to intercept the relative position of the current frame to the previous frame. Afterward, we combine ViBe+ algorithm to accurately detect and eliminate dynamic noise. Secondly, image semantic segmentation is performed based on YOLACT model. Image feature points are extracted from MAPLAB algorithm, pose estimation of the camera is performed and movement trajectory is recorded to complete a semantic map. Finally, through ablations study with common algorithms, the experimental results show that the proposed algorithm effectively avoids interference of dynamic noise information on VSLAM. Additionally, the constructed semantic map provides higher precision, fewer noise points, and pretty robustness.
ISSN:1024-123X
1563-5147
DOI:10.1155/2022/7307783