This paper proposes simple and efficient algorithms for implementation of attitude estimation and control based on data fusion using complementary filters taking into account sensors dynamics. First of all, we propose a passive form of the filter by fusing the measured inertial vectors and the gyro measurements in order to reconstruct real inertial vectors which can be used with any algebraic algorithm (TRIAD, QUEST, etc.) that leads to globally asymptotic attitude estimation. Thereafter, the same principle of data fusion is used to address the problem of attitude stabilization. Then, instead of using direct raw measurements in control law we propose a new solution that leads to accurate estimation of inertial vectors by using complementary filters based on sensors dynamics. The stability analysis of the error dynamics based on Lyapunov method proved that almost all trajectories converge asymptotically to the desired equilibrium point. Simulation results show the effectiveness and the performance of the proposed solutions.

In this paper a new method for attitude estimation of rigid body using line-based vision and a Multiplicative Extended Kaiman Filter (MEKF) is developed. A vision-based line-tracking algorithm that allows to detect and to track points and lines along sequence of images without drift is used. From this algorithm, we can get an implicit measure of the lines direction. The latter are then fused with gyro measurements using an observer designed on SO (3) in order to estimate attitude with gyro bias compensation. The gain matrices of the proposed observer are determined based on continuous-time MEKF. The problem of sign ambiguity related to the implicit measure of direction lines is addressed and a correction factor is used to remove this ambiguity. Simulation results has been presented to show the effectiveness of the proposed approach.