Pose and motion recovery from feature correspondences and a digital terrain map

Error analysis and attitude observability of a monocular GPS/visual odometry integrated navigation filter

In this paper, we present a method for the recovery of position and absolute attitude (including pitch, roll and yaw) using a novel fusion of monocular visual odometry and GPS measurements in a similar manner to a classic loosely coupled GPS/INS error state navigation filter. The proposed filter does not require additional restrictions or assumptions such as platform-specific dynamics, map matching, feature tracking, visual loop closing, gravity vector or additional sensors such as an inertial measurement unit or magnetic compass. An observability analysis of the proposed filter is performed, showing that the scale factor, position and attitude errors are fully observable under acceleration that is non-parallel to the velocity vector in the navigation frame. The observability properties of the proposed filter are demonstrated using numerical simulations. We conclude the article with an implementation of the proposed filter using real flight data collected from a Cessna 172 equipped with a downwards-looking camera and GPS, showing the feasibility of the algorithm in real-world conditions.

Direct method for video-based navigation using a digital terrain map

A novel vision-based navigation algorithm is proposed. The gray levels of two images, together with a Digital Terrain Map (DTM), are directly utilized to define constraints on the navigation parameters. The feasibility of the algorithm is examined both under a simulated environment and using real flight data.