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Abstract
This paper is concerned with the attitude/position estimation of a rigid-body using inertial and vision sensors. By employing the Newton-Euler method, a kinematic model is developed for the rigid-body by treating the inertial measurements as inputs. Based on the coordinate transformation, a nonlinear visual observation model is proposed by using the image coordinates of feature points as observations. Then the Extended Kalman filter (EKF) is utilized to estimate the attitude/position recursively. The effectiveness of the proposed algorithm is evaluated by simulation.
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