Monocular Visual-Inertial SLAM With IMU-Aided Hybrid Line Matching

Multisensor fusion simultaneous localization and mapping (SLAM) has gained popularity in the SLAM community due to its low cost and high real-time performance. Common point-feature-based visual-inertial SLAM systems often struggle in environments with weak textures or motion blur. By incorporating l...

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Bibliographic Details
Published in:IEEE sensors letters 2024-09, Vol.8 (9), p.1-4
Main Authors: Zha, Gongpu, Guan, Peiyu, Cao, Zhiqiang, Sun, Ting, Yu, Shijie
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Blurring
Cameras
Feature extraction
Image segmentation
improved enhanced line segment drawing (iELSED)
IMU-aided hybrid line matching
Inertial platforms
Matching
Multisensor fusion
Optimization
point-line based loop-closure detection
Real time
Redundancy
Sensor applications
Sensors
Simultaneous localization and mapping
Time measurement
visual-inertial simultaneous localization and mapping (SLAM)
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Summary:Multisensor fusion simultaneous localization and mapping (SLAM) has gained popularity in the SLAM community due to its low cost and high real-time performance. Common point-feature-based visual-inertial SLAM systems often struggle in environments with weak textures or motion blur. By incorporating line features, the accuracy and robustness of SLAM systems can be improved. However, challenges in line matching and increased processing time caused by line features have limited these improvements. To address the problem, we introduce a real-time monocular visual-inertial SLAM method with inertial measurement unit (IMU)-aided hybrid line matching, where the hybrid lines consist of elementary and recessive lines. Specifically, an IMU-aided hybrid line matching scheme is designed to determine the search space of line matching according to the IMU preintegration result. It scales down the search range effectively and thus improves the accuracy and speed of line matching. Also, an improved enhanced line segment drawing (iELSED) algorithm is utilized for efficient elementary line feature extraction, where the parameters of line features are adaptively adjusted with the number of extracted point features to avoid feature redundancy. In addition, we also extend the point-based loop-closure detection by introducing line features for higher accuracy of loop-closure detection. Experiment results demonstrate the effectiveness of the proposed method.
ISSN:2475-1472
2475-1472
DOI:10.1109/LSENS.2024.3435988