Abstract <p>To date one of the most accurate methods of solving the autonomous navigation problem relies on processing optical information captured during the motion of a vehicle. The existing optical flow processing methods based on the determination of the so-called velocity field allow us to estimate only projections of the vehicle’s linear and angular velocities. This, in turn, is only a part of the overall task of navigation: estimating the current coordinates of the vehicle and its spatial orientation parameters. Due to the limitations of such optical navigation systems (ONSs), it is proposed to integrate them. These systems offer the advantage of stable autonomous monitoring of linear and angular motion parameters over an arbitrary time interval with minimal hardware costs. The functionality of inertial navigation systems (INSs) provides the solution to the problem of autonomous navigation as a whole. Due to the unavoidable interference of various physical origins, which significantly distort measurements of the mentioned navigation systems (NSs), the considered integrated inertial optical system is synthesized using the methods of modern stochastic filtering theory, which are by far the most effective methods for estimating state parameters in a noisy environment. An extended Kalman filter, modified to take into account the noise correlation between the vehicle and observer, is chosen as the estimation algorithm for the navigation state vector based on measurements from the developed inertial-optical NS. The results of a numerical experiment illustrating the effectiveness of the proposed approach are presented.</p>