This work presents a hybrid method for navigation parameter estimation using sequential aerial images, where navigation parameters represent the position and velocity information of an aircraft for autonomous navigation. The proposed hybrid system is composed of two parts: relative position estimation and absolute position estimation. Computer simulation with two different sets of real aerial image sequences shows the effectiveness of the proposed hybrid parameter estimation algorithm.

The structure extraction problem is analyzed. The cooccurrence matrices (CMs) are the popular basis for this goal. We show that a binary preparation of an arbitrary periodical texture preserves its structure. This transformation decreases the computation time of analysis and the required memory. Twenty-two features adapted for detecting displacement vectors on binarized images are analyzed and compared. We suggest using the CM elements jointly as the united feature for this goal. We show that it is a stable detector for noisy images and simpler than well-known /spl chi//sup 2/ and /spl kappa/ statistics.

This paper presents a method for navigation parameter estimation using sequential aerial images, where navigation parameters represent the velocity and position information of an aircraft for autonomous navigation. The proposed navigation parameter estimation system is composed of two parts: relative position estimation and absolute position estimation. Relative position estimation recursively computes the current velocity and position of an aircraft by accumulating navigation parameters extracted from two successive aerial images. However, simple accumulation of parameter values decreases reliability of the extracted parameters as an aircraft goes on navigating, resulting in a large position error. Therefore absolute position estimation is required to compensate for position error generated in the relative position estimation step. A hybrid absolute position estimation algorithm combining image matching and digital elevation model (DEM) matching is presented. In image matching, line segment matching or Hausdorff distance (HD) matching is employed whereas in DEM matching a new algorithm for absolute position estimation by minimizing the variance of displacements is proposed. Computer simulation with real aerial image sequences shows the effectiveness of the proposed algorithm.