The automatic landmark identification is very important in autonomous robotnavigation tasks. In this paper, we use a monocular omnidirectional visionsystem to extract the image features and the conformal geometric algebra tocompute the projective invariants from such features. We show how these featurescan be used to compute projective and permutationp2-invariants from any kind ofomnidirectional vision system. Thep2-invariants represent scenesublandmarks, and a set of them characterize a landmark. The advantage of thisrepresentation is that the landmarks are more robust than the singlecross-ratio.