Fly360: Omnidirectional Obstacle Avoidance within Drone View

• URL: http://arxiv.org/abs/2603.06573v1
• Date: Fri, 06 Mar 2026 18:59:43 GMT
• Title: Fly360: Omnidirectional Obstacle Avoidance within Drone View
• Abstract summary: Obstacle avoidance in unmanned aerial vehicles (UAVs) has gained increasing attention with the growing focus on spatial intelligence.<n>Current obstacle-avoidance methods mainly depend on limited field-of-view sensors and are ill-suited for UAV scenarios.<n>This limitation motivates us to explore omnidirectional obstacle avoidance for panoramic drones with full-view perception.
• Score: 23.21774398877285
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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/

Related papers

• Instance-level Visual Active Tracking with Occlusion-Aware Planning [61.982298426203165]
  Visual Active Tracking (VAT) aims to control cameras to follow a target in 3D space.<n>VAT faces two key bottlenecks in real-world deployment: confusion from visually similar distractors and severe failure under occlusions.<n>We propose OA-VAT, a unified pipeline with three complementary modules.
  arXiv  Detail & Related papers  (2026-04-23T09:11:50Z)
• Vision-Guided MPPI for Agile Drone Racing: Navigating Arbitrary Gate Poses via Neural Signed Distance Fields [8.66703842496129]
  Drone racing requires tight coupling of perception, planning, and control under extreme agility.<n>Recent approaches rely on precomputed spatial reference trajectories or explicit 6-DoF gate pose estimation.<n>We propose a vision guided optimal control framework that enables reference-free agile flight through arbitrarily placed and oriented gates.
  arXiv  Detail & Related papers  (2026-03-07T13:18:07Z)
• SpectraSentinel: LightWeight Dual-Stream Real-Time Drone Detection, Tracking and Payload Identification [0.0903415485511869]
  The proliferation of drones in civilian airspace has raised urgent security concerns.<n>In response to the 2025 VIP Cup challenge tasks, we propose a dual-stream drone monitoring framework.<n>Our approach deploys independent You Only Look Once v11-nano (YOLOv11n) object detectors on parallel infrared (thermal) and visible (RGB) data streams.
  arXiv  Detail & Related papers  (2025-07-30T13:10:13Z)
• NOVA: Navigation via Object-Centric Visual Autonomy for High-Speed Target Tracking in Unstructured GPS-Denied Environments [56.35569661650558]
  We introduce NOVA, a fully onboard, object-centric framework that enables robust target tracking and collision-aware navigation.<n>Rather than constructing a global map, NOVA formulates perception, estimation, and control entirely in the target's reference frame.<n>We validate NOVA across challenging real-world scenarios, including urban mazes, forest trails, and repeated transitions through buildings with intermittent GPS loss.
  arXiv  Detail & Related papers  (2025-06-23T14:28:30Z)
• Open-World Drone Active Tracking with Goal-Centered Rewards [62.21394499788672]
  Drone Visual Active Tracking aims to autonomously follow a target object by controlling the motion system based on visual observations.<n>We propose DAT, the first open-world drone active air-to-ground tracking benchmark.<n>We also propose GC-VAT, which aims to improve the performance of drone tracking targets in complex scenarios.
  arXiv  Detail & Related papers  (2024-12-01T09:37:46Z)
• Monocular Obstacle Avoidance Based on Inverse PPO for Fixed-wing UAVs [29.207513994002202]
  Fixed-wing Unmanned Aerial Vehicles (UAVs) are one of the most commonly used platforms for the Low-altitude Economy (LAE) and Urban Air Mobility (UAM)<n>Classical obstacle avoidance systems, which rely on prior maps or sophisticated sensors, face limitations in unknown low-altitude environments and small UAV platforms.<n>This paper proposes a lightweight deep reinforcement learning (DRL) based UAV collision avoidance system.
  arXiv  Detail & Related papers  (2024-11-27T03:03:37Z)
• Radiance Field Learners As UAV First-Person Viewers [36.59524833437512]
  First-Person-View (FPV) holds immense potential for revolutionizing the trajectory of Unmanned Aerial Vehicles (UAVs) Traditional Neural Radiance Field (NeNeRF) methods face challenges such as sampling single points per granularity. We introduce FPV-NeRF, addressing these challenges through three key facets.
  arXiv  Detail & Related papers  (2024-08-10T12:29:11Z)
• Clustering-based Learning for UAV Tracking and Pose Estimation [0.0]
  This work develops a clustering-based learning detection approach, CL-Det, for UAV tracking and pose estimation using two types of LiDARs. We first align the timestamps of Livox Avia data and LiDAR 360 data and then separate the point cloud of objects of interest (OOIs) from the environment. The proposed method shows competitive pose estimation performance and ranks 5th on the final leaderboard of the CVPR 2024 UG2+ Challenge.
  arXiv  Detail & Related papers  (2024-05-27T06:33:25Z)
• UAV Obstacle Avoidance by Human-in-the-Loop Reinforcement in Arbitrary 3D Environment [17.531224704021273]
  This paper focuses on the continuous control of the unmanned aerial vehicle (UAV) based on a deep reinforcement learning method. We propose a deep reinforcement learning (DRL)-based method combined with human-in-the-loop, which allows the UAV to avoid obstacles automatically during flying.
  arXiv  Detail & Related papers  (2023-04-07T01:44:05Z)
• Fully Convolutional One-Stage 3D Object Detection on LiDAR Range Images [96.66271207089096]
  FCOS-LiDAR is a fully convolutional one-stage 3D object detector for LiDAR point clouds of autonomous driving scenes. We show that an RV-based 3D detector with standard 2D convolutions alone can achieve comparable performance to state-of-the-art BEV-based detectors.
  arXiv  Detail & Related papers  (2022-05-27T05:42:16Z)
• Dogfight: Detecting Drones from Drones Videos [58.158988162743825]
  This paper attempts to address the problem of drones detection from other flying drones variations. The erratic movement of the source and target drones, small size, arbitrary shape, large intensity, and occlusion make this problem quite challenging. To handle this, instead of using region-proposal based methods, we propose to use a two-stage segmentation-based approach.
  arXiv  Detail & Related papers  (2021-03-31T17:43:31Z)
• Vision-Based Mobile Robotics Obstacle Avoidance With Deep Reinforcement Learning [49.04274612323564]
  Obstacle avoidance is a fundamental and challenging problem for autonomous navigation of mobile robots. In this paper, we consider the problem of obstacle avoidance in simple 3D environments where the robot has to solely rely on a single monocular camera. We tackle the obstacle avoidance problem as a data-driven end-to-end deep learning approach.
  arXiv  Detail & Related papers  (2021-03-08T13:05:46Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.