Semantic trajectory planning for long-distant unmanned aerial vehicle navigation in urban environments
Name
mryll-iros20.pdf
Description
Accepted version
Size
5.36 MB
Format
Adobe PDF
Checksum (MD5)
c4985a19c514af8aa087505c9dee0994
Author(s)
Ryll, Markus
Ware, John W.
Carter, John
Roy, Nicholas
Date Issued
February 2021
Journal
IEEE International Conference on Intelligent Robots and Systems
Publisher
IEEE
Citation
Ryll, Markus, Ware, John W., Carter, John and Roy, Nicholas. 2021. "Semantic trajectory planning for long-distant unmanned aerial vehicle navigation in urban environments." IEEE International Conference on Intelligent Robots and Systems.
Version
Author's final manuscript
Abstract
© 2020 IEEE. There has been a considerable amount of recent work on high-speed micro-aerial vehicle flight in unknown and unstructured environments. Generally these approaches either use active sensing or fly slowly enough to ensure a safe braking distance with the relatively short sensing range of passive sensors. The former generally requires carrying large and heavy LIDARs and the latter only allows flight far away from the dynamic limits of the vehicle. One of the significant challenges for high-speed flight is the computational demand of trajectory planning at sufficiently high rates and length scales required in outdoor environments. We tackle both problems in this work by leveraging semantic information derived from an RGB camera on-board the vehicle. We first describe how to use semantic information to increase the effective range of perception on certain environment classes. Second, we present a sparse representation of the environment that is sufficiently lightweight for long distance path planning. We show how our approach outperforms more traditional metric planners which seek the shortest path, demonstrate the semantic planner's capabilities in a set of simulated and excessive real-world autonomous quadrotor flights in an urban environment.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/IROS45743.2020.9341441
