Attitude estimation using line-based vision and multiplicative extended Kalman filter

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.