3D orientation vector estimation for subcomponents of space object imagery

Xun Du; Stanley C. Ahalt; Bruce E. Stribling

doi:10.1117/12.280820

28 July 1997 3D orientation vector estimation for subcomponents of space object imagery

Xun Du, Stanley C. Ahalt, Bruce E. Stribling

Proceedings Volume 3068, Signal Processing, Sensor Fusion, and Target Recognition VI; (1997) https://doi.org/10.1117/12.280820
Event: AeroSense '97, 1997, Orlando, FL, United States

Abstract

We describe a model-based image analysis system which automatically estimates the 3D orientation vector of satellites and their sub-components by analyzing images obtained from a ground-based optical surveillance system. We adopt a two-step approach: pose estimates are derived from comparisons with a model database; pose refinements are derived from photogrammetric information. The model database is formed by representing each available training image by a set of derived geometric primitives. To obtain fast access to the model database and to increase the probability of early successful matching, a novel index hashing method is introduced. We present recent results which include our efforts at isolating and estimating orientation vectors from degraded imagery on a significant database of satellites. We also discuss the problems our system encounters with some of the images, and the solutions we are implementing to significantly improve the system.

Citation Download Citation

Xun Du, Stanley C. Ahalt, and Bruce E. Stribling "3D orientation vector estimation for subcomponents of space object imagery", Proc. SPIE 3068, Signal Processing, Sensor Fusion, and Target Recognition VI, (28 July 1997); https://doi.org/10.1117/12.280820

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