APEIRON-IND4

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细节，请访问APEIRON官方站点：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, UAVs）常被称为无人机，正日益被用作支撑各类应用的平台，涵盖监视、灾害响应、环境监测、无人机直播以及无人机物联网（Internet-of-Drones, IoD）等场景。在此背景下，无人机系统需在潜在未知且复杂的环境中自主执行任务。为此，深度学习算法被广泛用于实现基于传感器的高效感知，因此获取能够真实还原现实环境的全面数据集显得尤为关键。本研究推出APEIRON这款丰富的多模态空中数据集，它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，以及通过LTE模块（LTE module）获取的无线网络链路测量数据。本数据集同时包含感知与网络数据，可适用于典型的感知或通信应用，以及需要同时使用两类数据的跨学科应用。我们相信，本数据集将助力推动多媒体系统、计算机网络与机器人学交叉领域的多学科研究。本数据集的下载地址为：https://c3lab.github.io/Apeiron/}, booktitle = {第15届ACM多媒体系统大会论文集}, 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}}