  Autonomous flight in unknown environments requires precise planning for both
the spatial and temporal profiles of trajectories, which generally involves
nonconvex optimization, leading to high time costs and susceptibility to local
optima. To address these limitations, we introduce the Learning-Initialized
Trajectory Planner (LIT-Planner), a novel approach that guides optimization
using a Neural Network (NN) Planner to provide initial values. We first
leverage the spatial-temporal optimization with batch sampling to generate
training cases, aiming to capture multimodality in trajectories. Based on these
data, the NN-Planner maps visual and inertial observations to trajectory
parameters for handling unknown environments. The network outputs are then
optimized to enhance both reliability and explainability, ensuring robust
performance. Furthermore, we propose a framework that supports robust online
replanning with tolerance to planning latency. Comprehensive simulations
validate the LIT-Planner's time efficiency without compromising trajectory
quality compared to optimization-based methods. Real-world experiments further
demonstrate its practical suitability for autonomous drone navigation.

Robotics;Artificial Intelligence