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In this paper, aiming at application of vision-based mobile robot navigation, we present a novel method for detecting scale and rotation invariant interest points, coined polynomial local orientation tensor (PLOT). Our detector is based on the local orientation tensor, which is constructed from the polynomial expansion of the image signal. We first analyze the properties of the local orientation tensor of PLOT, and select a suitable tuning parameter to make the local orientation tensor extract invariant features. Automatic scale selection is also used in the computation of the local orientation tensor and the characteristic scale is selected to attain scale invariant features. Then, the true interest points are detected by the small eigenvalues of the orientation tensor. We evaluate the performance of our detector on the repeatability criteria and compare it with other existing approaches. Experimental results for PLOT show strong performance in different rotations, with varying scale changes and illumination changes in the real-world conditions.
In this paper, aiming at application of vision-based mobile robot navigation, we present a novel method for detecting scale and rotation invariant interest points, coined polynomial local orientation tensor (PLOT). Our detector is based on the local orientation tensor, which is constructed from the polynomial expansion of the image signal. We first analyze the properties of the local orientation tensor of PLOT, and select a suitable tuning parameter to make the local orientation tensor extract invariant features. Automatic scale selection is also used in the computation of the Then, the true interest points are detected by the small eigenvalues of the orientation tensor. We evaluate the performance of our detector on the repeatability criteria and compare it with other existing approaches. . Experimental results for PLOT show strong performance in different rotations, with varying scale changes and illumination changes in the real-world conditions.