Coupled Vision and Inertial Navigation for Pin - Point Landing

Our research task in NASA’s Mars Technology Program focuses on terrain relative state estimation during planetary descent to enable pin-point landing. In this task we have developed estimation algorithms that fuse inertial and visual measurements as well as computer vision algorithms that automatically extract landmark locations and terrain feature tracks through a sequence of images. These algorithms have been tested extensively with simulated data and data collected during multiple field test campaigns. This paper describes our recent development and testing of an algorithm for navigation state estimation during planetary descent to enable precision landing. The algorithm automatically produces 2D-to-3D correspondences between descent images and a surface map and 2D-to-2D correspondences through a sequence of descent images. These correspondences are combined with inertial measurements in an extended Kalman filter that estimates lander position, velocity and attitude as well as the time varying biases of the inertial measurements. The filter tightly couples inertial and camera measurements in a resource-adaptive and hence real-time capable fashion. Results from a sounding rocket test, covering the dynamic profile of typical planetary landing scenarios, show estimation errors of magnitude 0.16 m/s in velocity and 6.4 m in position at touchdown. These results vastly improve current state of the art and meet the requirements of future planetary exploration missions.