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We describe a method for visual odometry using a single camera based on an EKF framework. Previous work has shown that filtering based approaches can achieve accuracy performance comparable to that of optimisation methods providing that large numbers of features are used. However, computational requirements are signicantly increased and frame rates are low. We address this by employing higher level structure - in the form of planes - to efficiently parameterise features and so reduce the filter state size and computational load. Moreover, we extend a 1-point RANSAC outlier rejection method to the case of features lying on planes. Results of experiments with both simulated and real-world data demonstrate that the method is effective, achieving comparable accuracy whilst running at significantly higher frame rates.

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