Complementary filter design on the special orthogonal group SO(3)

Abstract

Abstract-This paper considers the problem of obtaining high quality pose estimation (position and orientation) from a combination of low cost sensors, such as an inertial measurement unit and vision sensor. A non-linear complementary filter is proposed that evolves on the Special Euclidean Group SE(3). Exponential stability of the filter is proved. Simulation results are presented to illustrate simplicity and demonstrate the performance of the proposed approach. Experimental results reinforce the convergence of the filter.

Iterative calibration method for inertial and magnetic sensors

“…In the literature, these IMUs are only used for orientation (attitude) estimation (see e.g. [4], [12] or [3] for an application to the control of mini-UAVs in closed loop). Some tentative work (using higher-end IMUs) address the problem of velocities estimation.…”

Wireless Sensing System for Long-Time Assistance in the Parkinson’s Disease

“…The algorithm is based on a time domain signal analysis. The raw signals of accelerometers and gyroscopes are fused together by using an orientation-estimation algorithm [3]. The sampling frequency (fs) is 60 Hz when a PC is used and 25 Hz when a smartphone is used.…”

The Navigation and Control technology inside the AR.Drone micro UAV

“…They are in fact estimated and compensated thanks to the vision system as presented in Section 4. Under this assumption, various algorithms for attitude estimation can been considered: Kalman filter, extended Kalman filter (see Vissière [2008]) or complementary filter in both linear and nonlinear implementations (see Mahony et al [2005], Metni et al [2006], Jung and Tsiotras [2007], Mahony et al [2008], Martin and Salaün [2008]). …”