Mounting Misalignment and Time Offset Self-Calibration Online Optimization Method for Vehicular Visual-inertial-wheel Odometer System

• Published in: IEEE transactions on instrumentation and measurement Vol. 73; p. 1
• Main Authors: Zhang, Hanxuan, Wang, Dingyi, Huo, Ju
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.01.2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Accuracy; Calibration; Cameras; Delay effects; Delays; Field tests; Global navigation satellite system; graph optimization; Inertial platforms; Misalignment; mounting misalignment; Odometers; Odometry; Offsets; Optimization; Optimization models; Parameters; pre-integration; self-calibration; Sensors; Time lag; time offset; Visual aspects; Visualization
• ISSN: 0018-9456; 1557-9662
• DOI: 10.1109/TIM.2024.3385827

The visual-inertial-wheel odometry system significantly enhances the positioning accuracy of ground vehicles in GNSS-deprived environments. To fully exploit the fusion capabilities of vision, inertial measurement units (IMU), and wheel odometers (referred to as odometry), precise calibration of their interrelated parameters is imperative. Conventional online calibration methods typically rely on a filtering framework, thereby limiting the potential improvement in online calibration accuracy. Therefore, we propose a self-calibrating online optimization approach based on the theory of pre-integration for mounting misalignments and time offsets. Distinct from existing pre-integration methods, a comprehensive IMU-odometer pre-integration model is derived, considering the misalignments of position and attitude between the IMU and odometer, odometer scale factors, and IMU-odometer time offsets. Subsequently, to address camera-IMU time delays, visual factors with time offsets are designed. Then, the IMU-odometer pre-integration and visual factors with time offsets are collectively incorporated into the graph-based optimization model, simultaneously optimizing spatial/temporal calibration parameters between sensors and the navigation state of the system. This overcomes the issue in existing online calibration methods that are influenced by the initial parameter values. Finally, both dataset and field test results indicate that our calibration method exhibits higher precision compared to other online calibration methods and offline tools. Our online calibration method achieves an error of approximately 0.013m in the X/Y axis position, about 0.30 degrees error in the Y/Z axis orientation, and a time delay error of approximately 0.44ms between sensors. Furthermore, our approach serves as a versatile model easily applicable to optimization-based visual-inertial-wheel odometry frameworks.