Long-Distance 3D Reconstructions Using Photogrammetry with Curiosity's ChemCam Remote Micro-Imager in Gale Crater (Mars) - Archive ouverte HAL
Article Dans Une Revue Remote Sensing Année : 2021

Long-Distance 3D Reconstructions Using Photogrammetry with Curiosity's ChemCam Remote Micro-Imager in Gale Crater (Mars)

Roger C Wiens
Nina L Lanza
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Résumé

The Mars Science Laboratory rover Curiosity landed in Gale crater (Mars) in August 2012. It has since been studying the lower part of the 5 km-high sedimentary pile that composes Gale's central mound, Aeolis Mons. To assess the sedimentary record, the MSL team mainly uses a suite of imagers onboard the rover, providing various pixel sizes and fields of view from close to longrange observations. For this latter, we notably use the Remote Micro Imager (RMI), a subsystem of the ChemCam instrument that acts as 700 mm-focal length telescope, providing the smallest angular pixel size of the set of cameras on the Remote Sensing Mast. The RMI allows observations of remote outcrops up to a few kilometers away from the rover. As retrieving 3D information is critical to characterize the structures of the sedimentary deposits, we describe in this work an experiment aiming at computing for the first time with RMI Digital Outcrop Models of these distant outcrops. We show that Structure-from-Motion photogrammetry can successfully be applied to suitable sets of individual RMI frames to reconstruct the 3D shape and relief of these distant outcrops. These results show that a dedicated set of observations can be envisaged to characterize the most interesting geological features surrounding the rover.
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Dates et versions

hal-03375441 , version 1 (12-10-2021)
hal-03375441 , version 2 (18-10-2021)

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Gwénaël Caravaca, Stéphane Le Mouélic, William Rapin, Gilles Dromart, Olivier Gasnault, et al.. Long-Distance 3D Reconstructions Using Photogrammetry with Curiosity's ChemCam Remote Micro-Imager in Gale Crater (Mars). Remote Sensing, 2021, Special Issue "Planetary 3D Mapping, Remote Sensing and Machine Learning", 13 (20), pp.4068. ⟨10.3390/rs13204068⟩. ⟨hal-03375441v1⟩
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