Estimation of IMU and MARG orientation using a gradient descent algorithm

By Sebastian O. H. Madgwick, Andrew J. L. Harrison, Ravi Vaidyanathan. International Conference on Rehabilitation Robotics, 2011

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Using Inertial Sensors for Position and Orientation Estimation (2017, neutral): “Two wellknown complementary filter implementations to estimate the orientation q are presented in [90] and [89].”
Keeping a Good Attitude: A Quaternion-Based Orientation Filter for IMUs and MARGs (2015, methodology): “in [26], the quaternion-based EKF proposed by Sabatini [24], and the algorithm embedded in the low-level processor of the AscTec quadrotor, whose output, in Euler angles form, is provided in the datasets.”
Trends Supporting the In-Field Use of Wearable Inertial Sensors for Sport Performance Evaluation: A Systematic Review (2018, methodology): “Examples of sensor fusion approaches are stochastic filtering, often implemented in the form of an Extended Kalman Filter [21,35] and complementary filtering [36,37].”
Very High Frame Rate Volumetric Integration of Depth Images on Mobile Devices (2015, methodology): “In this case we replace the visual rotation optimisation in both ICP and colour-based trackers with inertial sensor-based rotation obtained using the fusion algorithm of [10].”
Survey of Motion Tracking Methods Based on Inertial Sensors: A Focus on Upper Limb Human Motion (2017, methodology): “In some cases it has been used for gait analysis [36,37,79,93], in other cases only parts of the lower limbs were targeted, mainly for monitoring purposes in medical settings [94,96] or rehabilitation [44,59].”