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Machine Vision based Sample-Tube Localization for Mars Sample Return

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dc.contributor.author Daftry, Shreyansh
dc.contributor.author Ridge, B.
dc.contributor.author Seto, W.
dc.contributor.author Pham, T.
dc.contributor.author Illhardt, P.
dc.contributor.author Maggiolino, G.
dc.contributor.author Van der Merwe, M.
dc.contributor.author Brinkman, A.
dc.contributor.author Mayo, J.
dc.contributor.author Kulczyski, E.
dc.contributor.author Detry, R.
dc.date.accessioned 2022-03-01T00:44:26Z
dc.date.available 2022-03-01T00:44:26Z
dc.date.issued 2021-03-06
dc.identifier.citation 2021 IEEE Aerospace Conference, Big Sky, Montana, March 6-13, 2021
dc.identifier.clearanceno CL#21-0137
dc.identifier.uri http://hdl.handle.net/2014/54250
dc.description.abstract A potential Mars Sample Return (MSR) architecture is being jointly studied by NASA and ESA. As currently envisioned, the MSR campaign consists of a series of 3 missions: sample cache, fetch and return to Earth. In this paper, we focus on the fetch part of the MSR, and more specifically the problem of autonomously detecting and localizing sample tubes deposited on the Martian surface. Towards this end, we study two machine-vision based approaches: First, a geometrydriven approach based on template matching that uses hardcoded filters and a 3D shape model of the tube; and second, a data-driven approach based on convolutional neural networks (CNNs) and learned features. Furthermore, we present a large benchmark dataset of sample-tube images, collected in representative outdoor environments and annotated with ground truth segmentation masks and locations. The dataset was acquired systematically across different terrain, illumination conditions and dust-coverage; and benchmarking was performed to study the feasibility of each approach, their relative strengths and weaknesses, and robustness in the presence of adverse environmental conditions.
dc.description.sponsorship NASA/JPL en_US
dc.language.iso en_US
dc.publisher Pasadena, CA: Jet Propulsion Laboratory, National Aeronautics and Space Administration, 2021
dc.title Machine Vision based Sample-Tube Localization for Mars Sample Return
dc.type Preprint


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