Abstract
Abstract. Sensor fusion of a MEMS IMU with a magnetometer is a popular system design, because such 9-DoF (degrees of freedom) systems are capable of achieving drift-free 3D orientation tracking. However, these systems are often vulnerable to ambient magnetic distortions and lack useful position information; in the absence of external position aiding (e.g. satellite/ultra-wideband positioning systems) the dead-reckoned position accuracy from a 9-DoF MEMS IMU deteriorates rapidly due to unmodelled errors. Positioning information is valuable in many satellite-denied geomatics applications (e.g. indoor navigation, location-based services, etc.). This paper proposes an improved 9-DoF IMU indoor pose tracking method using batch optimization. By adopting a robust in-situ user self-calibration approach to model the systematic errors of the accelerometer, gyroscope, and magnetometer simultaneously in a tightly-coupled post-processed least-squares framework, the accuracy of the estimated trajectory from a 9-DoF MEMS IMU can be improved. Through a combination of relative magnetic measurement updates and a robust weight function, the method is able to tolerate a high level of magnetic distortions. The proposed auto-calibration method was tested in-use under various heterogeneous magnetic field conditions to mimic a person walking with the sensor in their pocket, a person checking their phone, and a person walking with a smartwatch. In these experiments, the presented algorithm improved the in-situ dead-reckoning orientation accuracy by 79.8–89.5 % and the dead-reckoned positioning accuracy by 72.9–92.8 %, thus reducing the relative positioning error from metre-level to decimetre-level after ten seconds of integration, without making assumptions about the user’s dynamics.
Highlights
In markerless close-range photogrammetry or laser scanning, the imaging instrument is often moved between tripod locations to achieve complete coverage of an object
Instead of requiring the sensor to be static, the local magnetic field should be constant and homogeneous. If this assumption is satisfied, the magnetometer can act as a lowpass filter that smooths out the sensed angular rate, while the gyroscope captures the high-frequency dynamics missed by the magnetometers
This paper presented a new total-system user self-calibration routine for a 9-DoF MEMS Inertial Measurement Unit (IMU)
Summary
In markerless close-range photogrammetry or laser scanning, the imaging instrument is often moved between tripod locations to achieve complete coverage of an object. A registration process is performed to combine the data. Such a nonlinear estimation process can benefit from good initial pose information (e.g. better than 1 metre for translation and 10 degrees for rotation (Bae, 2009)), which an Inertial Measurement Unit (IMU) can provide. IMUs are self-contained instruments capable of measuring accurate relative poses when the systematic errors are modelled well. To process the IMU data, conventional Kalman filter textbooks usually introduce the IMU mechanization equations as part of the dynamics model for real-time navigation. In this paper, IMU data will be treated as sensor measurements in a batch least-squares framework to obtain a globally smoothed navigation solution while compensating for relevant systematic errors simultaneously
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