Ming-Ze Ma

This work presents OmniRisk, an omnidirectional planning framework that learns trajectory-level risk offline for efficient onboard evasion and enables efficient onboard avoidance, with real-world flights demonstrating consecutive evasive maneuvers at relative encounter speeds up to 15 m/s without fine-tuning.

Achieving high-quality low-light imaging without the need for clean RGB data could revolutionize how we approach image enhancement in challenging environments.