The scientific objectives of the ExoMars rover are designed to answer several key questions in the search for life on Mars. In particular, the unique subsurface drill will address some of these, such as the possible existence and stability of subsurface organics. PanCam will establish the surface geological and morphological context for the mission, working in collaboration with other context instruments. Here, we describe the PanCam scientific objectives in geology, atmospheric science, and 3-D vision. We discuss the design of PanCam, which includes a stereo pair of Wide Angle Cameras (WACs), each of which has an 11-position filter wheel and a High Resolution Camera (HRC) for high-resolution investigations of rock texture at a distance. The cameras and electronics are housed in an optical bench that provides the mechanical interface to the rover mast and a planetary protection barrier. The electronic interface is via the PanCam Interface Unit (PIU), and power conditioning is via a DC-DC converter. PanCam also includes a calibration target mounted on the rover deck for radiometric calibration, fiducial markers for geometric calibration, and a rover inspection mirror. Key Words: Mars—ExoMars—Instrumentation—Geology—Atmosphere—Exobiology—Context. Astrobiology 17, 511–541.
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The upcoming launch of the European Space Agency (ESA) ExoMars 2020 rover signals a need for an analysis tool to be created which can exploit the multi-and hyperspectral data that will be returned by its Panoramic Camera (PanCam), Infrared Spectrometer for Mars (ISEM), and Close-UP Imager (CLUPI) instruments. Data processed by this analysis tool will be invaluable in (i) characterising the geology local to the ExoMars rover, (ii) relating ground-based observations to orbital Compact Reconnaissance Imaging Spectrometer for Mars (CRISM) data, (iii) detecting evidence of past habitability on Mars, and (iv) identifying drilling locations. PanCam, ISEM, and CLUPI offer spectral analysis capabilities in both spatial (140-1310 microns/pixel at 2 m working distance) and spectral (440-3300 nm) dimensions. We have developed the ExoMars Spectral Tool (ExoSpec) which functions as a GUI-based extension to ENVI + IDL and performs steps from image import and compilation into ENVI .dat format, flat-fielding, radiometric correction, radiance-toreflectance (R*) corrections using the in-scene Gretag MacBeth ColorChecker TM , and calculation of spectral parameters. We demonstrate the functionality of ExoSpec at its current stage of development and illustrate its utility with results from field expeditions to Mars analogue terrains in: (i) geothermally altered basalts in Námafjall, Iceland, and (ii) layered alluvial plains deposits in Hanksville, USA, using ExoMars PanCam, ISEM, and CLUPI emulator instruments.
Multispectral imaging instruments have been core payload components of Mars lander and rover missions for several decades. In order to place into context the future performance of the ExoMars Rosalind Franklin rover, we have carried out a detailed analysis of the spectral performance of three visible and near‐infrared (VNIR) multispectral instruments. We have determined the root mean square error (RMSE) between the expected multispectral sampling of the instruments and high‐resolution spectral reflectance data, using both laboratory spectral libraries and Mars orbital hyperspectral data. ExoMars Panoramic Camera (PanCam) and Mars2020 Perseverance Mastcam‐Z instruments have similar values of RMSE, and are consistently lower than for Mars Science Laboratory Mastcam, across both laboratory and orbital remote sensing data sets. The performance across mineral groups is similar across all instruments, with the lowest RMSE values for hematite, basalt, and basaltic soil. Minerals with broader, or absent, absorption features in these visible wavelengths, such as olivine, saponite, and vermiculite have overall larger RMSE values. Instrument RMSE as a function of filter wavelength and bandwidth suggests that spectral parameters that use shorter wavelengths are likely to perform better. Our simulations of the spectral performance of the PanCam instrument will allow the future use of targeted filter selection during ExoMars 2022 Rosalind Franklin operations on Mars.
<p><strong>Introduction:</strong>&#160; The ExoMars 2022 Rosalind Franklin rover is scheduled to be launched in summer 2022 with a suite of instruments to investigate the Martian surface and near sub-surface [1]. The context instruments: the Panoramic Camera (PanCam), composed of the two Wide Angle Cameras (WACs), and High Resolution Camera (HRC), and the Infrared Spectrometer for ExoMars (ISEM) will be imperative in the selection of drill and analysis sites. The PanCam stereo imaging system will be the primary mode of scientific observation during the mission with two multispectral WACs in the Visible to Near Infrared (VNIR, 440-1000 nm) range mounted at the top of the 2 meter mast [2]. Within the 36&#176; field of view of &#160;the PanCam WACs, HRC will provide 5&#176; field of view colour images at up to submillimetre resolutions [2]. Lastly, ISEM can them be utilised within the WAC/HRC field of views to provide 1&#176; spot size, hyper-spectral coverage in the Near to Mid (1150-3300 nm) Infrared range [3]. In preparation for the mission, spectral analysis tools are being developed to automate as much of the analysis process as is feasible to reduce time and effort costs during mission tactical planning and analysis, as well as improving ability to discriminate between different minerals of interest. Here we report on our effort to constrain the spectral and spatial response of the context instruments for ExoMars using Martian meteorite targets</p> <p><strong>Martian Meteorite Imaging:</strong> This study used instruments emulators for PanCam WAC, ISEM (extended to 300-2500 nm range to provide coverage of PanCam filter wavelengths for comparison) and HRC to investigate the spectral response of a variety of SNC meteorites, to determine the instrument spectral and spatial capabilities and build reliable mission analysis tools. Preliminary analysis has been undertaken on the largest of the Martian meteorite samples. Meteorites were imaged at minimum mission configuration, in semi-directional lighting and operated under mission-similar protocols [2]. The Shergottite Tissint, BM2012, M1, was imaged to assess the instrument ability to distinguish visual and spectral features in the fresh face of the specimen. The PanCam emulator data was first flat-fielded, and environmentally colour corrected and radiometrically corrected using the ExoSpec Software developed by the PanCam science team [4]. &#160;</p> <p><em>Preliminary Results.</em> The fresh face of Tissint is comprised of olivine macrocrysts with black glass veins [5]. These features can be distinguished visually in both the WAC and HRC images (Figure 1). These features, however, are too small to target alone with the hyperspectral instrumentation. To probe the spectral response of these regions a 530-570-670 decorrelation stretch &#160;was applied to highlight variation associated with a characteristic olivine spectral feature in this region, shown in Figure 2. This decorrelation stretch does show significant spectral variation between the dominant face material and the black glass veins.</p> <p><img src="data:image/png;base64, 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