APEIRON-IND2

This is a single run of APEIRON: a Multimodal Drone Dataset Bridging Perception and Network Data in Outdoor Environments. For more data and details visit: APEIRON (c3lab.github.io) If you use this dataset in an academic context, please cite the following work: @inproceedings{10.1145/3625468.3652186, author = {Barone, Nunzio and Brescia, Walter and Mascolo, Saverio and De Cicco, Luca}, title = {APEIRON: a Multimodal Drone Dataset bridging Perception and Network Data}, year = {2024}, publisher = {Association for Computing Machinery}, url = {https://doi.org/10.1145/3625468.3652186}, doi = {10.1145/3625468.3652186}, abstract = {Unmanned Aerial Vehicles (UAVs), commonly denoted as drones, are being increasingly adopted as platforms to enable applications such as surveillance, disaster response, environmental monitoring, live drone broadcasting, and Internet-of-Drones (IoD). In this context, drone systems are required to carry out tasks autonomously in potentially unknown and challenging environments. As such, deep learning algorithms are widely adopted to implement efficient perception from sensors, making the availability of comprehensive datasets capturing real-world environments important. In this work, we introduce APEIRON, a rich multimodal aerial dataset that simultaneously collects perception data from a stereocamera and an event based camera sensor, along with measurements of wireless network links obtained using an LTE module. The assembled dataset consists of both perception and network data, making it suitable for typical perception or communication applications, as well as cross-disciplinary applications that require both types of data. We believe that this dataset will help promoting multidisciplinary research at the intersection of multimedia systems, computer networks, and robotics fields. APEIRON is available at https://c3lab.github.io/Apeiron/}, booktitle = {Proceedings of the 15th ACM Multimedia Systems Conference}, keywords = {Open Dataset, UAV, Event camera, Network traces, Stereocamera}, location = {Bari, Italy}, series = {MMSys '24} }

本数据集为APEIRON单批次采集数据：一款面向户外环境、衔接感知与网络数据的多模态无人机数据集。如需获取更多数据与详细信息，请访问APEIRON官方页面：c3lab.github.io。
若在学术研究中使用本数据集，请引用如下文献：
@inproceedings{10.1145/3625468.3652186,
author = {Barone, Nunzio and Brescia, Walter and Mascolo, Saverio and De Cicco, Luca},
title = {APEIRON：一款衔接感知与网络数据的多模态无人机数据集},
year = {2024},
publisher = {美国计算机协会（Association for Computing Machinery, ACM)},
url = {https://doi.org/10.1145/3625468.3652186},
doi = {10.1145/3625468.3652186},
abstract = {无人驾驶航空器（Unmanned Aerial Vehicles, UAV，俗称无人机）正被日益广泛地应用于安防监控、应急救灾、环境监测、无人机实况直播以及无人机物联网（Internet-of-Drones, IoD）等场景。在此类场景中，无人机系统需要在未知且复杂的环境中自主完成任务。为此，深度学习算法被广泛用于实现基于各类传感器的高效感知任务，因此能够覆盖真实户外环境的全面数据集的可用性显得尤为重要。本研究推出APEIRON——一款丰富的多模态航空数据集，可同时采集立体相机（stereocamera）与事件相机（event based camera）传感器的感知数据，并搭配通过LTE模块获取的无线网络链路测量数据。本整合数据集同时包含感知与网络数据，可适配典型的感知类或通信类应用场景，同时也适用于同时需要两类数据的跨学科研究应用。我们相信本数据集将有助于推动多媒体系统、计算机网络与机器人学交叉领域的多学科研究。APEIRON数据集可通过https://c3lab.github.io/Apeiron/获取。},
booktitle = {第15届ACM多媒体系统会议论文集},
keywords = {开放数据集、UAV、事件相机、网络轨迹、立体相机},
location = {意大利巴里},
series = {MMSys '24}
}

搜集汇总

背景与挑战

背景概述

APEIRON-IND2是APEIRON多模态无人机数据集的一个单次运行版本，发布于2024年4月。该数据集同时收集了来自立体相机和事件相机的感知数据，以及通过LTE模块获取的网络链路测量数据，适用于无人机感知、通信和跨学科研究。数据集大小为13.1 GB，具有多模态特性，支持户外环境下的真实世界应用。