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This paper presents a Kalman filter (KF)-integrated optical flow method to measure the velocity of mobile robots using a downward-looking camera. Tests conducted earlier by the authors have shown that currently available differential optical flow methods (X. Song, L. D. Seneviratne, K. Althoefer, and Z. Song, “Vision-based velocity estimation for unmanned ground vehicles,” Int. J. Inf. Acquis., vol. 4, no. 4, pp. 303-315, 2007) require large image overlap for accurate velocity estimation. This constraint significantly limits the usefulness of this approach in practical applications. To overcome the problem of dealing with large image displacements, a KF is incorporated to efficiently predict the image transformations. Reducing the feature search area, the KF enables the differential optical flow method to rapidly converge and give accurate velocity estimates. The proposed method has been validated on a linear test rig under laboratory conditions and on a mobile platform in an outdoor field. Test results show good performance of the proposed method in velocity measurements with large image displacements. With this improvement, the required image overlap for feature tracking can be reduced approximately from 80% to 20%, resulting in a fourfold increase of the maximum measurable velocity of the mobile platform. The proposed method has good potential in velocity sensing for mobile robots, particularly in cases, where GPS and inertial measurement unit fail or are unavailable.