# Terrain-aware Low Altitude Path Planning

arXiv: https://arxiv.org/abs/2505.07141 · HTML: https://mlsanity.com/paper/2505.07141/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 May 2025.

## Built on by (most cited first, 1 of 1)

- [Next Generation Intelligent Low-Altitude Economy Deployments: The O-RAN Perspective](https://mlsanity.com/paper/2601.00257/) (2026)

## Abstract (arXiv)

In this paper, we study the problem of generating low-altitude path plans for nap-of-the-earth (NOE) flight in real time with only RGB images from onboard cameras and the vehicle pose. We propose a novel training method that combines behavior cloning and self-supervised learning, where the self-supervision component allows the learned policy to refine the paths generated by the expert planner. Simulation studies show 24.7% reduction in average path elevation compared to the standard behavior cloning approach.