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Completed TRL 3 (started at 2, targeting 3)
Autonomous landing requires the lander to locate a landing site (mapping) and to locate itself with respect to that landing site (localization). Current NASA autonomous landing technologies exhibit several key drawbacks: they cannot operate in unknown environments as they rely on previously collected information, they have degraded performance as they do not acknowledge correlations between mapping and localization, and they have decreased flexibility as they cannot autonomously select a landing site. This research seeks to eliminate these three drawbacks.
Simultaneous Localization and Mapping (SLAM) allows a spacecraft to refine its location and create a map of its surroundings using sensors such as cameras or Light Detection and Ranging (LIDAR) devices. This research investigates a landing system which creates high fidelity map of the surface using cameras, LIDAR, and an inertial sensor. This map can be created without any previous knowledge of the terrain, meaning the system can function in unknown environments. SLAM fully acknowledges correlations between localization and mapping, meaning that the solutions for both are of higher quality than methods which do not acknowledge these correlations. Furthermore, the research will develop methodologies for identifying an optimal landing site within the map and generate a trajectory towards that landing site using the lowest amount of fuel possible.
The research performed through this proposal can provide significant advancements for NASA’s autonomous landing capabilities. It allows landers to land in environments which are not well mapped through orbital imagery like Titan, or dynamic such as the surface of Europa. Furthermore, the ability to select a landing site provides significantly increased lander flexibility and autonomy.
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