Vision based navigation for autonomous planetary landers

Recent planetary lander missions to Mars, such as UK's Beagle 2, and NASA's Spirit and Opportunity have highlighted the need for further research on vision based navigation and hazard detection and avoidance systems for autonomous planetary landers in order to achieve safe, soft, and precise landings. Landing on the Moon, on Mars or other planetary bodies, close to a predetermined target landing spot, in an area of rough terrain, is a difficult and risky task. Accurate navigation relative to the planetary surface is necessary, together with the detection of possible hazards like boulders or steep slopes. This is being made possible by development in vision-based guidance techniques and on-board processing technology. Concepts are evolving from ground-based to autonomous GNC systems. Hazard avoidance systems for planetary landers are replacing hazard tolerance systems. Both lidar-based and vision-based GNC systems are currently being developed to support autonomous landings. The optimum mix of sensors and the best way to fuse sensor data is an important area of research for future planetary lander space missions. Kalman filters play an important role in fusing data from several sensors to give an optimal estimate of the dynamic state of the spacecraft system. Vision and Navigation data can be integrated using an EKF. This paper describes an EKF to estimate the position and the attitude quaternion of a planetary lander during the approach phase from high-gate to low-gate, using information from visual data integrated with inertial data.