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Fast and Robust Monocular Visua-Inertial Odometry Using Points and Lines.
1State Key Laboratory of Virtual Reality Technology and Systems, School of Automation Science and Eletrical Engineering, Beihang University, Beijing 100191, China. zy1703239@buaa.edu.cn.
Sensors (Basel, Switzerland)
|October 23, 2019
Summary
This study introduces a robust monocular visual odometry algorithm combining point, line, and IMU data. It improves Simultaneous Localization and Mapping (SLAM) accuracy and reliability, especially in challenging visual conditions.
Area of Science:
- Robotics
- Computer Vision
- Sensor Fusion
Background:
- Simultaneous Localization and Mapping (SLAM) struggles with featureless or fast-moving scenes.
- Point feature-based SLAM exhibits poor accuracy and robustness in challenging conditions.
Purpose of the Study:
- To develop a monocular visual odometry algorithm enhancing SLAM performance.
- To improve positioning accuracy and robustness in visually degraded environments.
Main Methods:
- Integrating point and line features with Inertial Measurement Unit (IMU) data.
- Constructing an environmental-feature map with geometric information.
- Utilizing IMU data for prior and scale information in visual localization.
Main Results:
- Achieved superior performance compared to OKVIS-mono and ORB-SLAM on EuRoC datasets.
- Demonstrated enhanced accuracy and speed in visual odometry.
- Successfully improved feature tracking in blurred or texture-less scenes.
Conclusions:
- The proposed point-line-IMU visual odometry algorithm offers significant improvements.
- The method provides a more accurate and robust SLAM solution.
- Effective for real-time applications requiring precise localization.
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