Long-Term Autonomy in Forest Environment using Self-Corrective SLAM

@article{Nevalainen2021LongTermAI,
  title={Long-Term Autonomy in Forest Environment using Self-Corrective SLAM},
  author={Paavo Nevalainen and Parisa Movahedi and Jorge Pe{\~n}a Queralta and Tomi Westerlund and Jukka Heikkonen},
  journal={ArXiv},
  year={2021},
  volume={abs/2101.00043}
}
Vehicles with prolonged autonomous missions have to maintain environment awareness by simultaneous localization and mapping (SLAM). Closed loop correction is substituted by interpolation in rigid body transformation space in order to systematically reduce the accumulated error over different scales. The computation is divided to an edge computed lightweight SLAM and iterative corrections in the cloud environment. Tree locations in the forest environment are sent via a potentially limited… 
Intelligent Robotics and Embedded Systems at the University of Turku
  • Computer Science
  • 2021
TLDR
The key research directions and recent developments within the TIERS Lab at the Faculty of Technology, University of Turku, Finland are edge computing, autonomous robots and multi-robot systems, and a key focus is on embedding intelligence through lightweight machine learning and dynamic offloading in edge devices and mobile robots.

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