Estimation of attitudes from a low-cost miniaturized inertial platform using Kalman Filter-based sensor fusion algorithm

Abstract

Due to costs, size and mass, commercially available inertial navigation systems are not suitable for small, autonomous flying vehicles like ALEX and other UAVs. In contrast, by using modern MEMS (or of similar class) sensors, hardware costs, size and mass can be reduced substantially. However, low-cost sensors often suffer from inaccuracy and are influenced greatly by temperature variation. In this work, such inaccuracies and dependence on temperature variations have been studied, modelled and compensated in order to reach an adequate quality of measurements for the estimation of attitudes. This has been done applying a Kalman Filter-based sensor fusion algorithm that combines sensor models, error parameters and estimation scheme. Attitude estimation from low-cost sensors is first realized in a Matlab/Simulink platform and then implemented on hardware by programming the micro controller and validated. The accuracies of the estimated roll and pitch attitudes are well within the stipulated accuracy level of ±5 • for the ALEX. However, the estimation of heading, which is mainly derived from the magnetometer readings, seems to be influenced greatly by the variation in local magnetic field.

A DCM Based Attitude Estimation Algorithm for Low-Cost MEMS IMUs

“…In many algorithms, other measurements are needed in addition to gyroscopes and accelerometers in order to estimate gyroscope biases. The most commonly used sensors are triaxial magnetometers [2,[9][10][11][12][13][14][15][16]25] and satellite navigation [3,5,[17][18][19][20]. In addition to our work, few filters [21][22][23][24]30] have been able to estimate gyroscope biases without extra sensors in addition to the triaxial accelerometer and gyroscope.…”

“…Most of these consist of various Kalman filter solutions [2,3,5,12,13,16,25], usually extended Kalman filters (EKF) [7,9,10,15,17,18,20,26], and some unscented Kalman filters (UKF) [14,19,27], though some non-Kalman filter solutions also exist [1,4,11,21,[28][29][30] as well as some geometric methods [31][32][33][34]. In addition, Chao et al [35] have carried out a comparative study of low-cost IMU filters.…”

“…Therefore, the integration of triaxial accelerometers and gyroscopes cannot provide an absolute heading angle. This is commonly overcome using an extra sensor, usually a triaxial magnetometer [2,[9][10][11][12][13][14][15][16] or satellite navigation [3,5,[17][18][19][20]. Triaxial magnetometers offer a good solution if the magnetic field measurement can be trusted.…”

Performance analysis of a Kalman Filter based attitude estimator for a Quad Rotor UAV

“…When the either the process to be estimated or the measurements are non-linear functions of the states being estimated, Extended Kalman Filter (EKF) provides an approximate solution to the estimation problem. EKF has been a popular tool for attitude estimation of different UAV platforms fitted with MEMS sensors [3], [13]. Of the different implementations, we study the sensor fusion approach where an EKF is used to optimally fuse gyroscopes and accelerometer measurements to obtain an estimate of Θ of QRUAV.…”

Methods for monitoring and controlling multi-rotor micro-UAVs position and orientation based on LabVIEW

“…As the result, a map including 41 cm error could be generated using the image taken by the helicopter [6] . Haitao Xiang and Lei Tian developed an unmanned aerial vehicle to monitor the application of turf grass glyphosate, which used the INS/GPS navigation to provide estimation of position and attitude [7] . They developed a navigation system using inertial measurement unit sensor, global positioning system, and sensor fusion technology to detect the position and attitude of UAV.…”