The selection of the Discovery Program InSight landing site took over four years from initial identification of possible areas that met engineering constraints, to downselection via targeted data from orbiters (especially Mars Reconnaissance SPAC 11214 layout: Small Condensed v.2.1 file: spac321.tex (ELE) class: spr-small-v1.2 v.2016/06/09 Prn:2016/12/02; 14:37 p. 1/91» « d o c to p i c : R e v i e w P a p e rn u m b e r i n g s t y l e : C o n t e n t O n l yr e f e r e n c e s t y l e : a p s » latitude (initially 15°S-5°N and later 3°N-5°N for solar power and thermal management of the spacecraft), ellipse size (130 km by 27 km from ballistic entry and descent), and a load bearing surface without thick deposits of dust, severely limited acceptable areas to western Elysium Planitia. Within this area, 16 prospective ellipses were identified, which lie ∼600 km north of the Mars Science Laboratory (MSL) rover. Mapping of terrains in rapidly acquired CTX images identified especially benign smooth terrain and led to the downselection to four northern ellipses. Acquisition of nearly continuous HiRISE, additional Thermal Emission Imaging System (THEMIS), and High Resolution Stereo Camera (HRSC) images, along with radar data confirmed that ellipse E9 met all landing site constraints: with slopes <15°at 84 m and 2 m length scales for radar tracking and touchdown stability, low rock abundance (<10 %) to avoid impact and spacecraft tip over, instrument deployment constraints, which included identical slope and rock abundance constraints, a radar reflective and load bearing surface, and a fragmented regolith ∼5 m thick for full penetration of the heat flow probe. Unlike other Mars landers, science objectives did not directly influence landing site selection. AUTHOR'S PROOF

[1] The size-frequency distributions of rocks >1.5 m diameter fully resolvable in High Resolution Imaging Science Experiment (HiRISE) images of the northern plains follow exponential models developed from lander measurements of smaller rocks and are continuous with rock distributions measured at the landing sites. Dark pixels at the resolution limit of Mars Orbiter Camera thought to be boulders are shown to be mostly dark shadows of clustered smaller rocks in HiRISE images. An automated rock detector algorithm that fits ellipses to shadows and cylinders to the rocks, accurately measured (within 1-2 pixels) rock diameter and height (by comparison to spacecraft of known size) of $10 million rocks over >1500 km 2 of the northern plains. Rock distributions in these counts parallel models for cumulative fractional area covered by 30-90% rocks in dense rock fields around craters, 10-30% rock coverage in less dense rock fields, and 0-10% rock coverage in background terrain away from craters. Above $1.5 m diameter, HiRISE resolves the same population of rocks seen in lander images, and thus size-frequency distributions can be extrapolated along model curves to estimate the number of rocks at smaller diameters. Extrapolating sparse rock distributions in the Phoenix landing ellipse indicate <1% chance of encountering a potentially hazardous rock during landing or that could impede the opening of the solar arrays. Extrapolations further suggest rocks large enough to depress the ground ice table and small enough to be picked up or pushed by the robotic arm should be present within reach for study after landing.

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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.