Qingdong Li

Learning to Initialize Trajectory Optimization for Vision-Based Autonomous Flight in Unknown Environments

Autonomous flight in unknown environments requires precise spatial and temporal trajectory planning, often involving computationally expensive nonconvex optimization prone to local optima. To overcome these challenges, we present the Neural-Enhanced Trajectory Planner (NEO-Planner), a novel approach