Vision-aided inertial navigation for spacecraft entry, descent, and landing
IEEE Transactions on Robotics
Landmark detection for autonomous spacecraft landing on mars
ISMIS'11 Proceedings of the 19th international conference on Foundations of intelligent systems
Vision-based absolute navigation for descent and landing
Journal of Field Robotics
Survey of advances in guidance, navigation, and control of unmanned rotorcraft systems
Journal of Field Robotics
High-precision, consistent EKF-based visual-inertial odometry
International Journal of Robotics Research
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In this paper we describe an extended Kalman filter algorithm for estimating the pose and velocity of a spacecraft during entry, descent, and landing. The proposed estimator combines measurements of rotational velocity and acceleration from an inertial measurement unit (IMU) with observations of a priori mapped landmarks, such as craters or other visual features, that exist on the surface of a planet. The tight coupling of inertial sensory information with visual cues results in accurate, robust state estimates available at a high bandwidth. The dimensions of the landing uncertainty ellipses achieved by the proposed algorithm are three orders of magnitude smaller than those possible when relying exclusively on IMU integration. Extensive experimental and simulation results are presented, which demonstrate the applicability of the algorithm on real-world data and analyze the dependence of its accuracy on several system design parameters. © 2007 Wiley Periodicals, Inc.