PanoAir: A Panoramic Visual-Inertial SLAM with Cross-Time Real-World UAV Dataset

• URL: http://arxiv.org/abs/2604.00852v1
• Date: Wed, 01 Apr 2026 13:05:19 GMT
• Title: PanoAir: A Panoramic Visual-Inertial SLAM with Cross-Time Real-World UAV Dataset
• Abstract summary: Existing Visual-Inertial SLAM methods rely on sensors with limited fields of view.<n>We propose a panoramic VI-SLAM framework that exploits the omnidirectional FoV.<n>Our method achieves superior accuracy, robustness, and consistency compared to existing approaches.
• Score: 3.1200868230538803
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Accurate pose estimation is fundamental for unmanned aerial vehicle (UAV) applications, where Visual-Inertial SLAM (VI-SLAM) provides a cost-effective solution for localization and mapping. However, existing VI-SLAM methods mainly rely on sensors with limited fields of view (FoV), which can lead to drift and even failure in complex UAV scenarios. Although panoramic cameras provide omnidirectional perception to improve robustness, panoramic VI-SLAM and corresponding real-world datasets for UAVs remain underexplored. To address this limitation, we first construct a real-world panoramic visual-inertial dataset covering diverse flight conditions, including varying illumination, altitudes, trajectory lengths, and motion dynamics. To achieve accurate and robust pose estimation under such challenging UAV scenarios, we propose a panoramic VI-SLAM framework that exploits the omnidirectional FoV via the proposed panoramic feature extraction and panoramic loop closure, enhancing feature constraints and ensuring global consistency. Extensive experiments on both the proposed dataset and public benchmarks demonstrate that our method achieves superior accuracy, robustness, and consistency compared to existing approaches. Moreover, deployment on embedded platform validates its practical applicability, achieving comparable computational efficiency to PC implementations. The source code and dataset are publicly available at https://drive.google.com/file/d/1lG1Upn6yi-N6tYpEHAt6dfR1uhzNtWbT/view

Related papers

• Multi-UAV Active Sensing with Information Gain-based Planning and Belief Fusion [0.0]
  Unmanned aerial vehicles (UAVs) are increasingly used for active sensing and information gathering in spatially distributed environments.<n>This paper presents a real-world validation of a multi-UAV active sensing framework for probabilistic binary terrain mapping.
  arXiv  Detail & Related papers  (2026-06-09T15:25:32Z)
• Beyond Matching to Tiles: Bridging Unaligned Aerial and Satellite Views for Vision-Only UAV Navigation [51.286599397552756]
  We present Bearing-UAV, a vision-driven cross-view navigation method that jointly predicts UAV absolute location and heading from neighboring features.<n>We also present Bearing-UAV-90k, a benchmark for evaluating cross-view localization and navigation.
  arXiv  Detail & Related papers  (2026-03-23T16:17:39Z)
• Aerial Vision-Language Navigation with a Unified Framework for Spatial, Temporal and Embodied Reasoning [5.517595398768408]
  We present a unified aerial VLN framework that operates solely on ego monocular RGB observations and natural language instructions.<n>This task holds promise for real-world applications such as low-altitude inspection, search-and-rescue, and autonomous aerial delivery.
  arXiv  Detail & Related papers  (2025-12-09T14:25:24Z)
• Scaling Up Occupancy-centric Driving Scene Generation: Dataset and Method [54.461213497603154]
  Occupancy-centric methods have recently achieved state-of-the-art results by offering consistent conditioning across frames and modalities.<n>Nuplan-Occ is the largest occupancy dataset to date, constructed from the widely used Nuplan benchmark.<n>We develop a unified framework that jointly synthesizes high-quality occupancy, multi-view videos, and LiDAR point clouds.
  arXiv  Detail & Related papers  (2025-10-27T03:52:45Z)
• 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)
• More Clear, More Flexible, More Precise: A Comprehensive Oriented Object Detection benchmark for UAV [58.89234732689013]
  CODrone is a comprehensive oriented object detection dataset for UAVs that accurately reflects real-world conditions.<n>It also serves as a new benchmark designed to align with downstream task requirements.<n>We conduct a series of experiments based on 22 classical or SOTA methods to rigorously evaluate CODrone.
  arXiv  Detail & Related papers  (2025-04-28T17:56:02Z)
• InCrowd-VI: A Realistic Visual-Inertial Dataset for Evaluating SLAM in Indoor Pedestrian-Rich Spaces for Human Navigation [2.184775414778289]
  InCrowd-VI is a visual-inertial dataset specifically designed for human navigation in indoor pedestrian-rich environments.<n>It features 58 sequences totaling a 5 km trajectory length and 1.5 hours of recording time, including RGB, stereo images, and IMU measurements.<n>Ground-truth trajectories, accurate to approximately 2 cm, are provided in the dataset, originating from the Meta Aria project machine perception SLAM service.
  arXiv  Detail & Related papers  (2024-11-21T17:58:07Z)
• Monocular BEV Perception of Road Scenes via Front-to-Top View Projection [57.19891435386843]
  We present a novel framework that reconstructs a local map formed by road layout and vehicle occupancy in the bird's-eye view. Our model runs at 25 FPS on a single GPU, which is efficient and applicable for real-time panorama HD map reconstruction.
  arXiv  Detail & Related papers  (2022-11-15T13:52:41Z)
• Towards Scale Consistent Monocular Visual Odometry by Learning from the Virtual World [83.36195426897768]
  We propose VRVO, a novel framework for retrieving the absolute scale from virtual data. We first train a scale-aware disparity network using both monocular real images and stereo virtual data. The resulting scale-consistent disparities are then integrated with a direct VO system.
  arXiv  Detail & Related papers  (2022-03-11T01:51:54Z)
• DMRVisNet: Deep Multi-head Regression Network for Pixel-wise Visibility Estimation Under Foggy Weather [0.0]
  Fog, as a kind of common weather, frequently appears in the real world, especially in the mountain areas. Current methods use professional instruments outfitted at fixed locations on the roads to perform the visibility measurement. We propose an innovative end-to-end convolutional neural network framework to estimate the visibility.
  arXiv  Detail & Related papers  (2021-12-08T13:31:07Z)
• Multi-View Fusion of Sensor Data for Improved Perception and Prediction in Autonomous Driving [11.312620949473938]
  We present an end-to-end method for object detection and trajectory prediction utilizing multi-view representations of LiDAR and camera images. Our model builds on a state-of-the-art Bird's-Eye View (BEV) network that fuses voxelized features from a sequence of historical LiDAR data. We extend this model with additional LiDAR Range-View (RV) features that use the raw LiDAR information in its native, non-quantized representation.
  arXiv  Detail & Related papers  (2020-08-27T03:32:25Z)

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.