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Estimation, Search, and Planning (ESP) Research Group

Osprey: Autonomous Aerial Surveying

Rowan has developed an autonomous aerial surveying platform, Osprey, in collaboration with ESP and the Dynamic Robot Systems (DRS) group at ORI. Osprey can perform multimission autonomous surveys of completely unknown 3D structures and is a great example of the research done in both ESP and DRS. You can watch a video of it in action on YouTube and read the paper submitted to Field Robotics on arXiv. This work was in collaboration with Nived Chebrolu, Yifu (Ethan) Tao, and Maurice Fallon.

Authors
  1. R. Border
  2. N. Chebrolu
  3. Y. Tao
  4. J. D. Gammell
  5. M. Fallon
Title
Osprey: Multi-session autonomous aerial mapping with lidar-based slam and next best view planning
Publication
Journal
Field Robotics
Date
Notes
In Revision, Manuscript #FR-23-0016
Videos
Video
PDFs
PDF
arXiv
Google Scholar
Google Scholar

Abstract

Aerial mapping systems are important for many surveying applications (e.g., industrial inspection or agricultural monitoring). Aerial platforms that can fly GPS-guided preplanned missions semi-autonomously are already widely available but fully autonomous systems can significantly improve efficiency. Autonomously mapping complex 3D structures requires a system that performs online mapping and mission planning. This paper presents Osprey, an autonomous aerial mapping system with state-of-the-art multi-session LiDAR-based mapping capabilities. It enables a non-expert operator to specify a bounded target area that the aerial platform can then map autonomously over multiple flights. Field experiments with Osprey demonstrate that this system can achieve greater map coverage of large industrial sites than manual surveys with a pilot-flown aerial platform or a terrestrial laser scanner (TLS). Three sites, with a total ground coverage of 2528 m2 and a maximum height of 27 m, were mapped in separate missions using 112 minutes of autonomous flight time. True colour maps were created from images captured by Osprey using pointcloud and NeRF reconstruction methods. These maps provide useful data for structural inspection tasks.