A landmark-based autonomous navigation scheme is presented for pinpoint planetary landing. The dynamic model is built on the basis of measurements from Inertial Measurement Unit. Measurement models of landmarks with known coordinates and landmarks with unknown coordinates extracted from sequential descent images are developed and used to calculated the state corrections in Extend Kalman Filter, respectively. Then, the corrections are fused by a covariance intersection fusion algorithm to perform state updates. The tight coupling of the two types of landmark observations yields accurate and robust state estimates. Extensive simulations are performed, which confirm the validity of the proposed navigation scheme and analyze the effects of factors, such as the horizonal position errors and the densities of landmarks with known coordinates and the roughness of the landing surface, on the navigation accuracy.