AbstractThe need for autonomous location estimation in the form of optical navigation is an essential requirement for forthcoming deep space missions. While crater-based navigation might work well with larger bodies littered with craters, small sub-kilometer bodies do not necessarily have them. We have developed a new pose estimation method for absolute navigation based on photometric local feature extraction techniques thus making it suitable for missions that cannot rely on craters. The algorithm can be used by a navigation filter in conjunction with relative pose estimation such as visual odometry for additional robustness and accuracy. To estimate the position and orientation of the spacecraft in the asteroid-fixed coordinate frame, it uses navigation camera images in combination with other readily available information, such as orientation relative to the stars and the current time for an initial estimate of the asteroid rotation state. Evaluation of the algorithm when using different feature extractors is performed, on one hand, using Monte Carlo simulations and, on the other hand, using actual images taken by the Rosetta spacecraft orbiting the comet 67P/Churyumov–Gerasimenko. Our analysis, where four different feature extraction methods (AKAZE, ORB, SIFT, SURF) were compared, showed that AKAZE is most promising in terms of stability and accuracy.
Feature Extraction for Regression Problems and an Example Application for Pose Estimation of a Face
