• Corpus ID: 204777580

SLAM Performance on Embedded Robots Undergraduate Student Research: Individual Project

@inproceedings{Ghalehshahi2019SLAMPO,
  title={SLAM Performance on Embedded Robots Undergraduate Student Research: Individual Project},
  author={Nima Shoghi Ghalehshahi and Ramyad Hadidi and Hyesoon Kim},
  year={2019}
}
We explore whether it is possible to run the popular ORBSLAM2 algorithm (simultaneous localization and mapping) in real-time on the Raspberry Pi 3B+ for use in embedded robots. We use a modified version of ORB-SLAM2 on the Pi and a laptop to measure the performance and accuracy of the algorithm on the EuRoC MAV dataset. We see similar accuracy between the two machines, but the Pi is about 10 times slower. Finally, we explore optimizations that can be applied to speed up execution on the Pi. We… 

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A survival of the fittest strategy that selects the points and keyframes of the reconstruction leads to excellent robustness and generates a compact and trackable map that only grows if the scene content changes, allowing lifelong operation.
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