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Visual Localization for Autonomous eVTOL Based on Semantic Map

Senwei Xiang, Ting Wang, Minxiang Ye, Yifei Zhang, Anhuan Xie, Zhejiang Lab
Weimin Yang, Yiwu Industrial and Commercial College

https://doi.org/10.4050/F-0079-2023-18193

Abstract:
The emerging concept of urban air mobility (UAM) and advanced air mobility (AAM) has made autonomous electric vertical takeoff and landing (eVTOL) aircraft a focal point for both academic research and commercial application. Achieving localization without the use of global navigation satellite systems (GNSS) is crucial to achieve full autonomy. In this paper, we present our exploration and progress on visual localization for autonomous eVTOL using semantic maps. By extracting semantic features from bottom-view images of the ground, we can create robust representations of flight routes that enable reliable and accurate localization even in dynamic environments. Taking inspiration from high-precision maps used in self-driving cars, we have designed a three-stage method that leverages massive flight data collected by eVTOLs during regular flights. Through on-board mapping and on-cloud merging, we can build up-to-date semantic maps of flight routes, which is further used for onboard localization. Real-world experiments conducted on an eVTOL prototype named ZJ-Copter show the localization accuracy is less than 2 meters over a flight distance of approximately 2.0 kilometers. We believe that our method has the potential to serve as an alternative localization solution for autonomous eVTOLs.


Visual Localization for Autonomous eVTOL Based on Semantic Map

  • Presented at Forum 79
  • 6 pages
  • SKU # : F-0079-2023-18193
  • Unmanned VTOL Aircraft and Rotorcraft

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Visual Localization for Autonomous eVTOL Based on Semantic Map

Authors / Details:
Senwei Xiang, Ting Wang, Minxiang Ye, Yifei Zhang, Anhuan Xie, Zhejiang Lab
Weimin Yang, Yiwu Industrial and Commercial College