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An Autonomous Vehicle Navigation System Based on Inertial and Visual Sensors
Xingxing Guang1,2, Yanbin Gao3, Henry Leung4
1College of Automation, Harbin Engineering University, Harbin 150001, China. xingxing.guang@ucalgary.ca.
Sensors (Basel, Switzerland)
|September 8, 2018
Summary
Strapdown inertial navigation systems (SINS) suffer from gyroscope drift errors. Combining Inertial Measurement Unit (IMU) data with camera-based line features enhances navigation accuracy and reliability for autonomous vehicles.
Area of Science:
- Robotics
- Navigation Systems
- Computer Vision
Background:
- Strapdown inertial navigation systems (SINS) are crucial for autonomous vehicles but accumulate significant errors due to gyroscope drift during prolonged operation.
- Uncorrected drift in SINS leads to substantial navigation inaccuracies, limiting their standalone utility for long-duration missions.
Purpose of the Study:
- To develop an autonomous navigation system that mitigates SINS drift errors by integrating visual data.
- To enhance the accuracy, stability, and reliability of navigation systems without external aiding.
Main Methods:
- Fusion of Inertial Measurement Unit (IMU) data with line feature parameters extracted from camera imagery.
- Development of an inertial-visual navigation system maintaining full autonomy.
Main Results:
- Demonstrated mitigation of SINS random drift errors through sensor fusion.
- Significant improvements in navigation accuracy and system stability were observed in experimental trials.
- The proposed system maintained reliable navigation performance.
Conclusions:
- The proposed inertial-visual navigation approach effectively compensates for SINS drift.
- Integrating IMU and visual line features offers a robust solution for autonomous vehicle navigation.
- This method enhances navigation system performance while preserving autonomy.
Keywords:
Kalman Filterautonomous vehicleinertial-visual fusion methodstrapdown inertial navigation systemMore Related Videos
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