Keeping a Good Attitude: A Quaternion-Based Orientation Filter for IMUs and MARGs

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.

On the Direct Kinematics Problem of Parallel Mechanisms

“…However, the algorithm where also the yaw orientation of the manipulator platform is needed requires the usage of a magnetic sensor, which, in general, is very imprecise. Several information filters, such as Kalman filter or complementary filters, were proposed to obtain the optimal measurement data fusion (see, e.g., [32,33]). These filters can perform very quickly (between 1.3 and 7 s [32]), but they do not calculate the correct yaw orientation with the first measurement value.…”

“…Several information filters, such as Kalman filter or complementary filters, were proposed to obtain the optimal measurement data fusion (see, e.g., [32,33]). These filters can perform very quickly (between 1.3 and 7 s [32]), but they do not calculate the correct yaw orientation with the first measurement value. Instead, the calculated yaw orientation only converges towards the correct value within several measurements.…”

Indoor mobile robot attitude estimation with MEMS gyroscope

“…Rotation vector at the moment t can be obtained from gyroscope readings (scaled in rad/s), using quaternion mathematics, commonly applied to rotation, orientation and tracking problems in navigational systems [9][10][11][12]. For any sufficiently small dt, a rotation vector can be derived directly from 3-axis gyroscope reading: (2) where: rv -rotation vector, gx, gy, gz -gyroscope readings (axes X, Y and Z) scaled to rad/s.…”

Building up an Inertial Navigation System Using Standard Mobile Devices

“…v is a set of unit vectors measured in the body frame, For the solution of this problem, multiple algorithms have been proposed, which can be classified into deterministic and optimal ones [26]. For the deterministic algorithms, the attitude is computed by using a minimal set of measured data and solving non-linear equations.…”

“…The TRIAD algorithm is a deterministic algorithm [27], where two measured non-parallel reference vectors are normalized and combined to construct two triads of orthonormal vectors. In contrast, QUEST is an optimal algorithm where Davenport's q-method is applied to find the optimal quaternion which is a transformation of Wahba's loss function [26,27]. In addition, there are many KF (Kalman Filter) based approaches.…”