Abstract
Orientation estimation using low cost sensors is an important task for Micro Aerial Vehicles (MAVs) in order to obtain a good feedback for the attitude controller. The challenges come from the low accuracy and noisy data of the MicroElectroMechanical System (MEMS) technology, which is the basis of modern, miniaturized inertial sensors. In this article, we describe a novel approach to obtain an estimation of the orientation in quaternion form from the observations of gravity and magnetic field. Our approach provides a quaternion estimation as the algebraic solution of a system from inertial/magnetic observations. We separate the problems of finding the “tilt” quaternion and the heading quaternion in two sub-parts of our system. This procedure is the key for avoiding the impact of the magnetic disturbances on the roll and pitch components of the orientation when the sensor is surrounded by unwanted magnetic flux. We demonstrate the validity of our method first analytically and then empirically using simulated data. We propose a novel complementary filter for MAVs that fuses together gyroscope data with accelerometer and magnetic field readings. The correction part of the filter is based on the method described above and works for both IMU (Inertial Measurement Unit) and MARG (Magnetic, Angular Rate, and Gravity) sensors. We evaluate the effectiveness of the filter and show that it significantly outperforms other common methods, using publicly available datasets with ground-truth data recorded during a real flight experiment of a micro quadrotor helicopter.
Highlights
The accurate estimation of the orientation of a rigid body, relative to an inertial frame, is required for a wide range of applications
The delta quaternion ∆qmag is affected by the noise of the magnetometer, which is filtered by using the procedure we adopted for ∆qacc, switching between Linear intERPolation (LERP) and Spherical Linear intERPolation (SLERP)
We compare our method against the orientation estimation proposed by Madgwick et al in [26], the quaternion-based Extended Kalman filtering (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
Summary
The accurate estimation of the orientation of a rigid body, relative to an inertial frame, is required for a wide range of applications. We propose a deterministic approach to solve Whaba’s problem [1] given gravity and magnetic field observations provided by the MARG sensor This method returns an estimation of the attitude in quaternion form without leading to ambiguous results and avoiding singularity problems. We propose a new approach to a complementary filter for fusing together gyroscope data with acceleration and magnetic field measurements from IMU and MARG sensors to obtain an orientation estimation in quaternion form. This algorithm is meant to be used onboard MAVs because of its low computational burden, robustness, and fast convergence.
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