Random features vs Harris Corners in Real-Time Visual Egomotion Estimation
Nuno Moutinho, Ricardo Poley Martins Ferreira, Alexandre J. M. Bernardino, José António Gaspar · 2010
We compare Randomly Selected (RanSel) features with Harris Corners within a visual egomotion estimation framework. Harris corners have been extensivelly used in visual egomotion estimation systems due to a good tracking stability. However, to compute these features the whole image has to be processed. Instead, we propose the use of randomly selected points which are virtually costless to obtain. Despite tracking individual RanSel features is not as stable as Harris corners, we show that, when integrated in a time-filtering scheme, they provide similar results at a much faster rate. We have performed experiments using a synthetic setup with ground-truth and discuss the advantages of using RanSel features. 1.