Nano Quadcopter Obstacle Avoidance with a Lightweight Monocular Depth Network

Research output: Contribution to journalConference articleScientificpeer-review

8 Downloads (Pure)

Abstract

In this paper, we propose an obstacle avoidance solution for a 34-gram quadcopter equipped with a monocular camera. The perception of obstacles is tackled by a lightweight convolutional neural network predicting a dense depth map from a captured grey-scale image. The depth network performs self-supervised learning and thus requires no ground-truth labels that are costly to acquire. Based on the depth map, the control strategy is implemented by a behavior state machine that balances the efficiency to explore the environment and the safety of avoiding obstacles. In real-world flight experiments, our solution demonstrates the efficacy of predicting trust-worthy depth maps and a stable control strategy in various cluttered environments.

Original languageEnglish
Pages (from-to)9312-9317
Number of pages6
JournalIFAC-PapersOnLine
Volume56
Issue number2
DOIs
Publication statusPublished - 2023
Event22nd IFAC World Congress - Yokohama, Japan
Duration: 9 Jul 202314 Jul 2023

Keywords

  • Autonomous robotic systems
  • Embedded robotics
  • Flying robots
  • Monocular depth prediction
  • Perception and sensing
  • Self-supervised learning

Fingerprint

Dive into the research topics of 'Nano Quadcopter Obstacle Avoidance with a Lightweight Monocular Depth Network'. Together they form a unique fingerprint.

Cite this