VQF: A Milestone in Accuracy and Versatility of 6D and 9D Inertial Orientation Estimation

Laidig, Daniel; Weygers, Ive; Bachhuber, Simon; Seel, Thomas

doi:10.23919/fusion49751.2022.9841356

Cited by 3 publications

(1 citation statement)

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“…However, in diverse and low-cost MEMS inertial sensor devices, data quality is difficult to match that of commercial inertial modules, and the adaptively obtained parameters are difficult to transfer between different devices and usage scenarios, making such algorithms difficult to apply on a large scale. The VQF [ 30 ] decoupled the heading error from the inclination error in attitude state representation to reject magnetic disturbances and provided precise inclination correction using specific force measurements. The results on the publicly available dataset [ 31 ] showed greatly improved accuracy and robustness compared to traditional filtering methods.…”

Section: Introductionmentioning

confidence: 99%

Robust Attitude and Heading Estimation under Dynamic Motion and Magnetic Disturbance

Wang

et al. 2023

Micromachines

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Robust and accurate attitude and heading estimation using Micro-Electromechanical System (MEMS) Inertial Measurement Units (IMU) is the most crucial technique that determines the accuracy of various downstream applications, especially pedestrian dead reckoning (PDR), human motion tracking, and Micro Aerial Vehicles (MAVs). However, the accuracy of the Attitude and Heading Reference System (AHRS) is often compromised by the noisy nature of low-cost MEMS-IMUs, dynamic motion-induced large external acceleration, and ubiquitous magnetic disturbance. To address these challenges, we propose a novel data-driven IMU calibration model that employs Temporal Convolutional Networks (TCNs) to model random errors and disturbance terms, providing denoised sensor data. For sensor fusion, we use an open-loop and decoupled version of the Extended Complementary Filter (ECF) to provide accurate and robust attitude estimation. Our proposed method is systematically evaluated using three public datasets, TUM VI, EuRoC MAV, and OxIOD, with different IMU devices, hardware platforms, motion modes, and environmental conditions; and it outperforms the advanced baseline data-driven methods and complementary filter on two metrics, namely absolute attitude error and absolute yaw error, by more than 23.4% and 23.9%. The generalization experiment results demonstrate the robustness of our model on different devices and using patterns.

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Section: Introductionmentioning

confidence: 99%