April 2, 2024, 7:50 p.m. | Jincheng Zhang, Artur Wolek, Andrew R. Willis

cs.CV updates on arXiv.org arxiv.org

arXiv:2401.06407v2 Announce Type: replace-cross
Abstract: This article presents an analysis of current state-of-the-art sensors and how these sensors work with several mapping algorithms for UAV (Unmanned Aerial Vehicle) applications, focusing on low-altitude and high-speed scenarios. A new experimental construct is created using highly realistic environments made possible by integrating the AirSim simulator with Google 3D maps models using the Cesium Tiles plugin. Experiments are conducted in this high-realism simulated environment to evaluate the performance of three distinct mapping algorithms: (1) …

abstract aerial algorithms analysis applications art article arxiv construct cs.cv cs.ro current drone environments experimental low mapping sensors speed state type unmanned aerial vehicle work

Data Architect

@ University of Texas at Austin | Austin, TX

Data ETL Engineer

@ University of Texas at Austin | Austin, TX

Lead GNSS Data Scientist

@ Lurra Systems | Melbourne

Senior Machine Learning Engineer (MLOps)

@ Promaton | Remote, Europe

Software Engineer, Machine Learning (Tel Aviv)

@ Meta | Tel Aviv, Israel

Senior Data Scientist- Digital Government

@ Oracle | CASABLANCA, Morocco