Vision based navigation algorithms estimate the position and attitude of a sensor platform by tracking stationary features in the neighboring environment across multiple image frames captured with an on-board camera. The set of feature matches between two frames is used to compute camera motion using algorithms based on multi-view geometry. The presence of bad feature matches in the data can introduce significant errors in the computed values of the rotation and translation. In this article, we present a robust estimation method to remove the false matched features. Performance of the proposed method is illustrated with numerical examples and compared with conventional Ransac approach. Reduction in computational load is observed for typical datasets without loss of performance making the scheme attractive for real-time implementations.