Three-Dimensional Drone Exploration with Saliency Prediction in Real Unknown Environments

Ming Ru Xie, Shing Yun Jung, Kuan Wen Chen*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

In this paper, we propose a three-dimensional autonomous drone exploration system (ADES) with a lightweight and low-latency saliency prediction model to explore unknown environments. Several studies have applied saliency prediction in drone exploration. However, these studies are not sufficiently mature. For example, the computational complexity and the size of the developed prediction models have not been considered. In addition, some studies have only proposed saliency prediction models without actually applying them to drones. The ADES system proposed in this paper has a small and fast saliency prediction model and uses a novel drone exploration approach based on visual-inertial odometry to solve the practical problems encountered during drone exploration, such as collisions with and the repeated exploration of salient objects. The proposed ADES system performs comparably to the state-of-the-art, multiple-discontinuous-image saliency prediction network TA-MSNet and enables drones to explore unknown environments with high efficiency.

Original languageEnglish
Article number488
JournalAerospace
Volume10
Issue number5
DOIs
StatePublished - May 2023

Keywords

  • automation
  • drone exploration
  • location awareness
  • prediction methods
  • UAV exploration

Fingerprint

Dive into the research topics of 'Three-Dimensional Drone Exploration with Saliency Prediction in Real Unknown Environments'. Together they form a unique fingerprint.

Cite this