English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

With the increasing prevalence of drones in various industries, thenavigation and tracking of unmanned aerial vehicles (UAVs) in challengingenvironments, particularly GNSS-denied areas, have become crucial concerns. Toaddress this need, we present a novel multi-LiDAR dataset specifically designedfor UAV tracking. Our dataset includes data from a spinning LiDAR, twosolid-state LiDARs with different Field of View (FoV) and scan patterns, and anRGB-D camera. This diverse sensor suite allows for research on new challengesin the field, including limited FoV adaptability and multi-modality dataprocessing.  The dataset facilitates the evaluation of existing algorithms and thedevelopment of new ones, paving the way for advances in UAV trackingtechniques. Notably, we provide data in both indoor and outdoor environments.We also consider variable UAV sizes, from micro-aerial vehicles to morestandard commercial UAV platforms. The outdoor trajectories are selected withclose proximity to buildings, targeting research in UAV detection in urbanareas, e.g., within counter-UAV systems or docking for UAV logistics.  In addition to the dataset, we provide a baseline comparison with recentLiDAR-based UAV tracking algorithms, benchmarking the performance withdifferent sensors, UAVs, and algorithms. Importantly, our dataset shows thatcurrent methods have shortcomings and are unable to track UAVs consistentlyacross different scenarios.

Towards Robust UAV Tracking in GNSS-Denied Environments: A Multi-LiDAR Multi-UAV Dataset