Detection of obstacles on runway using ego-motion compensation and tracking of significant features

The paper proposes a method for obstacle detection on a runway for autonomous navigation and landing of an aircraft. Detection is done in the presence of extraneous features such as tire marks. Suitable features are extracted from the image and warping using approximately known camera and plane parameters is performed in order to compensate ego-motion as far as possible. Residual disparity after warping is estimated using an optical flow algorithm. Features are tracked from frame to frame so as to obtain more reliable estimates of their motion. Corrections are made to motion parameters with the residual disparities using a robust method, and features having large residual disparities are signalled as obstacles. Sensitivity analysis of the procedure is also studied. A Bayesian framework is used at every stage so that the confidence in the estimates can be determined.

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