[1] NASA's Mars Exploration Rover (MER) Mission will place a total of 20 cameras (10 per rover) onto the surface of Mars in early 2004. Fourteen of the 20 cameras are designated as engineering cameras and will support the operation of the vehicles on the Martian surface. Images returned from the engineering cameras will also be of significant importance to the scientific community for investigative studies of rock and soil morphology. The Navigation cameras (Navcams, two per rover) are a mast-mounted stereo pair each with a 45°square field of view (FOV) and an angular resolution of 0.82 milliradians per pixel (mrad/pixel). The Hazard Avoidance cameras (Hazcams, four per rover) are a body-mounted, front-and rear-facing set of stereo pairs, each with a 124°square FOV and an angular resolution of 2.1 mrad/pixel. The Descent camera (one per rover), mounted to the lander, has a 45°square FOV and will return images with spatial resolutions of $4 m/pixel. All of the engineering cameras utilize broadband visible filters and 1024 Â 1024 pixel detectors.

The Mars Science Laboratory (MSL) Mars Hand Lens Imager (MAHLI) investigation will use a 2-megapixel color camera with a focusable macro lens aboard the rover, Curiosity, to investigate the stratigraphy and grain-scale texture, structure, mineralogy, and morphology of geologic materials in northwestern Gale crater. Of particular interest is the stratigraphic record of a ∼5 km thick layered rock sequence exposed on the slopes of Aeolis Mons (also known as Mount Sharp). The instrument consists of three parts, a camera head mounted on the turret at the end of a robotic arm, an electronics and data storage assembly located inside the rover body, and a calibration target mounted on the robotic arm shoulder azimuth actuator housing. MAHLI can acquire in-focus images at working distances from ∼2.1 cm to infinity. At the minimum working distance, image pixel scale is ∼14 µm per pixel and very coarse silt grains can be resolved. At the working distance of the Mars Exploration Rover Microscopic Imager cameras aboard Spirit and Opportunity, MAHLI's resolution is comparable at ∼30 µm per pixel. Onboard capabilities include autofocus, autoexposure, sub-framing, video imaging, Bayer pattern color interpolation, lossy and lossless compression, focus merging of up to 8 focus stack images, white light and longwave ultraviolet (365 nm) illumination of nearby subjects, and 8 gigabytes of non-volatile memory data storage.

The fine-scale textures of rock surfaces reflect a complex interplay of geologic processes that include those related to rock formation, exposure, weathering, and erosion. Typically, such features are studied in two dimensions using forensic geological techniques from scales from those visible to the naked eye to those in a wider-area context. Rock surface metrology is an emergent technique in which extremely fine-scale textural information is measured in 3D using laboratory instruments. Here we describe its application on the surface of Mars. With the advent of sub-millimeter resolution (14-100 µm per pixel) imaging on Mars, a relatively low-cost (i.e., camera-based) approach for computing quantitative relief models (QRM) of rock surfaces has been investigated. We present preliminary assessment of 20 different martian rock surfaces using this QRM technique; the observations have implications regarding the depositional, diagenetic, and weathering/erosional history of sedimentary rocks on Mars. The Mars Science Laboratory (MSL) Curiosity rover landed in Gale crater, Mars, to investigate a portion of the 5-km-thick stratigraphic section of largely sedimentary rocks exposed within the crater. These rocks display records of depositional and diagenetic environments thought to be ~3.6 billion years old and include fluvial conglomerates; fluvial, deltaic, and eolian sandstones; and lacustrine mudstones [1]. The Mars Hand Lens Imager (MAHLI) is a 2-megapixel Bayer pattern micro-filtered color camera with a focusable macro lens mounted on the turret at the end of Curiosity's robotic arm [2]. We used MAHLI to acquire sets of 5 overlapping frames arranged in a "+" pattern with ~31 µm/pixel resolution to compute QRMs at scales never before possible on natural planetary surfaces [3]. These QRM's allow analysis and interpretation of geologic surfaces at horizontal scales as fine as 100 µm, akin to non-destructive engineering metrology approaches. In principle, the quantitative textural information that QRMs convey for exposed rock surfaces on Mars is tied directly to the cumulative processes that formed, emplaced, and modified them. Thus, our QRM analysis of 20 different Mars rock surfaces offers a new opportunity to extract information about the depositional, diagenetic, and in situ weathering processes on Mars at spatial scales greater than 0.1 mm, extending the array of information about the role of diagenesis and other processes on Mars from mm-scale chemistry (i.e., ChemCam on Curiosity). We generated a series of QRM datasets, starting with a high-silica sandstone known as Greenstone (Sol 1130) and continuing to our latest rock surface targets named Belle Lake (Sol 1586) and Misery (Sol 1593). We analyzed the spatial and vertical distribution of rock surface 'texture elements' at multiple scales, from 0.1 mm to several mm in an effort to unravel their geologic histories. Critical to understanding the geologic phenomena captured in the QRMs are quantitative comparisons with terrestrial reference surfaces, which is an effo...

Increasing the level of spacecraft autonomy is essential for broadening the reach of solar system exploration. Computer vision has and will continue to play an important role in increasing autonomy of both spacecraft and Earthbased robotic vehicles. This article addresses progress on computer vision for planetary rovers and landers and has four main parts. First, we review major milestones in the development of computer vision for robotic vehicles over the last four decades. Since research on applications for Earth and space has often been closely intertwined, the review includes elements of both. Second, we summarize the design and performance of computer vision algorithms used on Mars in the NASA/JPL Mars Exploration Rover (MER) mission, which was a major step forward in the use of computer vision in space. These algorithms did stereo vision and visual odometry for rover navigation and feature tracking for horizontal velocity estimation for the landers. Third, we summarize ongoing research to improve vision systems for planetary rovers, which includes various aspects of noise reduction, FPGA implementation, and vision-based slip perception. Finally, we briefly survey other opportunities for computer vision to impact rovers, landers, and orbiters in future solar system exploration missions.

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