NASA's Mars Exploration Rover (MER) missions will land twin rovers on the surface of Mars in 2004. These rovers will have the ability to navigate safely through unknown and potentially hazardous terrain, using autonomous passive stereo vision to detect potential terrain hazards before driving into them. Unfortunately, the computational power of currently available radiation hardened processors limits the amount of distance (and therefore science) that can be safely achieved by any rover in a given time frame.

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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Portable mobile robots, in the size class of 20 kg or less, could be extremely valuable as autonomous reconnaissance platforms in urban hostage situations and disaster relief. We have developed a prototype urban robot on a novel chassis with articulated tracks that enable stair climbing and scrambling over rubble. Autonomous navigation capabilities of the robot include stereo vision-based obstacle avoidance, visual servoing to user-designated goals, and autonomous vision-guided stair climbing. The system was demonstrated in an urban reconnaissance mission scenario at Fort Sam Houston in October 1999. A two-axis scanning laser rangefinder has been developed and will be integrated in the coming year for indoor mapping and position estimation. This paper describes the robot, its performance in field trials, and some of the technical challenges that remain to enable fieldable urban reconnaissance robots.

Dynamic scene perception is currently limited to detection ofmoving objectsfrom a static plaform or scenes with flat backgrounds. We discuss novel real-time methods to segment moving objects in the motionfieldformed by a moving camerahobotic plaform with mostly translational motion. Our solution does not erplicitly require any egomotion knowledge, thereby making the solution opplicoble to mobile outdoor robot problems where no IMU information is mailable. We address two problems in dynomic scene perception on the move, first using only 2D monoculor grayscale images, and second where 3D range information from stereo is olso available. Our solution involves real-time optical flow computations, followed by optical jlow field preprocessing to highlight moving object boundaries. In the case where range data from stereo is computed, a 3 0 opticalflow field is estimated by combining range information with 2 0 optical flow estimates, followed by a similar 3 0 flow field preprocessing step. A segmentation of the jlow field using fastfloodfilling then identifies eveiy moving object in the scene with a unique lobel. This novel algorithm is apected to be the critical first step in robust recognition of moving vehicles ondpeople from mobile outdoor robots, and therefore offers a robust solution to the problem of dynamic scene perception in the presence of certain kinds of robot motion. It is envisioned that our algorithm will benefit robot scene perception in urban environments for scient&, commercial and defense applications. Results of our real-time algorithm (see video) on a mobile robot in a scene with a single moving vehicle ore presented.