Landmark-based navigation using projective invariants

Landmark-based navigation usually relies on the identification and subsequent recognition of a number of environment objects, that are deemed adequate in describing the workspace structure. This process is inherently difficult in practice, due to the many different poses of an object that may be encountered in navigational trials. To alleviate for that, we propose an approach that employs projective invariants computed on quintuples of points as worldspace landmarks. Such quantities remain invariant under different camera positions and provide for effective description of the workspace structure. In order to identify potential corresponding quintuples in image frames, we introduce a simple test based on the covariance matrix estimate of each quintuple. With this test, we effectively by-pass the calculation of point correspondences. Since the above test indicates correspondence between quintuples, and not between their individual points, we subsequently employ a permutation projective invariant for quintuple recognition. Our approach has been extensively evaluated using synthetic as well as real environments. The results obtained verify its robustness along with its applicability in robotic navigation