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- W4296246967 abstract "An important and emerging component of planetary exploration is sample retrieval and return to Earth. Obtaining and analyzing rock samples can provide unprecedented insight into the geology, geo-history and prospects for finding past life and water. Current methods of exploration rely on mission scientists to identify objects of interests and this presents major operational challenges. Finding objects of interests will require systematic and efficient methods to quickly and correctly evaluate the importance of hundreds if not thousands of samples so that the most interesting are saved for further analysis by the mission scientists. In this paper, we propose an automated information theoretic approach to identify shapes of interests using a library of predefined interesting shapes. These predefined shapes maybe human input or samples that are then extrapolated by the shape matching system using the Superformula to judge the importance of newly obtained objects. Shape samples are matched to a library of shapes using the eigenfaces approach enabling categorization and prioritization of the sample. The approach shows robustness to simulated sensor noise of up to 20%. The effect of shape parameters and rotational angle on shape matching accuracy has been analyzed. The approach shows significant promise and efforts are underway in testing the algorithm with real rock samples." @default.
- W4296246967 created "2022-09-19" @default.
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- W4296246967 date "2017-09-14" @default.
- W4296246967 modified "2023-10-16" @default.
- W4296246967 title "An Information Theoretic Approach to Sample Acquisition and Perception in Planetary Robotics" @default.
- W4296246967 doi "https://doi.org/10.48550/arxiv.1709.04617" @default.
- W4296246967 hasPublicationYear "2017" @default.
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