Statistics – Computation
Scientific paper
Jul 1994
adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=1994spie.2221...41l&link_type=abstract
Proc. SPIE Vol. 2221, p. 41-55, Acquisition, Tracking, and Pointing VIII, Michael K. Masten; Larry A. Stockum; Morris M. Birnbau
Statistics
Computation
Scientific paper
In a typical planetary spacecraft mission it is of interest to autonomously detect features on a planetary surface and to be able to track them. Various algorithms exist to track a part of an image in a continuous sequence, for instance correlation algorithms. However, these algorithms tend to be computational intensive, and in addition, they lack autonomy. Furthermore they are not robust toward occlusion, changed lighting conditions, and changes in vantage point. Therefore a more reliable strategy to track planetary terrains is needed. This is one of the major research topics of the Autonomous Feature and Star Tracking (AFAST) project at Jet Propulsion Laboratory, Pasadena. This paper presents an alternative method of feature tracking, namely by extraction of such features in the images that are reoccurring in consecutive images and that are invariant to various parameters. With these candidate features, it is possible to navigate the spacecraft based on identified terrain data. A novel methodology is proposed to track planetary surfaces by recognizing feature constellations, a method similar to those employed in recognizing star constellations in a star tracker.
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