Fly360: Omnidirectional Obstacle Avoidance within Drone View

TL;DR

Fly360 introduces a novel omnidirectional obstacle avoidance system for panoramic drones, enabling full-view spatial awareness and collision-free navigation in complex environments, outperforming traditional forward-view methods.

Contribution

The paper presents Fly360, a two-stage perception-decision pipeline with a fixed random-yaw training strategy for full-view obstacle avoidance in UAVs, addressing a previously underexplored problem setting.

Findings

Fly360 achieves stable omnidirectional obstacle avoidance in simulations and real-world tests.

It outperforms forward-view baselines across multiple flight tasks.

The approach effectively converts panoramic RGB images into depth maps for robust navigation.

Abstract

Obstacle avoidance in unmanned aerial vehicles (UAVs), as a fundamental capability, has gained increasing attention with the growing focus on spatial intelligence. However, current obstacle-avoidance methods mainly depend on limited field-of-view sensors and are ill-suited for UAV scenarios which require full-spatial awareness when the movement direction differs from the UAV's heading. This limitation motivates us to explore omnidirectional obstacle avoidance for panoramic drones with full-view perception. We first study an under explored problem setting in which a UAV must generate collision-free motion in environments with obstacles from arbitrary directions, and then construct a benchmark that consists of three representative flight tasks. Based on such settings, we propose Fly360, a two-stage perception-decision pipeline with a fixed random-yaw training strategy. At the perception stage, panoramic RGB observations are input and converted into depth maps as a robust intermediate representation. For the policy network, it is lightweight and used to output body-frame velocity commands from depth inputs. Extensive simulation and real-world experiments demonstrate that Fly360 achieves stable omnidirectional obstacle avoidance and outperforms forward-view baselines across all tasks. Our model is available at https://zxkai.github.io/fly360/