Rotation invariance of existing binary local descriptors is achieved by constructing the descriptor relative to a dominant orientation

which is a major source of matching errors. A novel rotation-invariant binary local descriptor is proposed together with its matching method. Sampling points are equally spaced on circles concentric to the keypoint. The intensity comparison results of these sampling point pairs are concatenated into binary descriptor in a rotation-invariant ma- nner. By matching for all rotations

the descriptor is rotation-invariant. Combined with a multi-scale feature detector

scale invariance can also be achieved by sub-pixel sampling. Experimental results on standard evaluation datasets show that the proposed descriptor outperforms other state-of-the-art binary descriptors under rotation

scale or illumination transformation

as the error-prone dominant orientation calculation is avoided.