Real-Time Terrain Mapping and Processing for Safe Landing via Deep Neural Networks

This research proposes a new integrated framework for identifying and prioritizing safe landing locations in real time to make inflight divert maneuvers. The state-of-the-art algorithms for landing zone selection only use local terrain features such as slopes and roughness; little research has focused on algorithms for an integrated system-based selection of landing zones. In response to this challenge, we propose a novel deep-learning-based framework to leverage on-ground training to enable the onboard algorithms to quickly detect hazards and identify safe landing zones with inflight divert maneuvers in consideration.

The proposed approach for this project significantly improves the real-time system-level hazard detection performance during the descent phase. The algorithms can be directly integrated with path planning, enabling safe autonomous landing for future aerospace missions, including NASA’s lunar, Mars, and other planetary missions.

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