Skymask matching aided positioning using sky-pointing fisheye camera and 3d city models in urban canyons

Max Jwo Lem Lee, Shang Lee, Hoi Fung Ng, Li Ta Hsu

Research output: Journal article publicationLetterAcademic researchpeer-review

2 Citations (Scopus)

Abstract

3D-mapping-aided (3DMA) global navigation satellite system (GNSS) positioning that improves positioning performance in dense urban areas has been under development in recent years, but it still faces many challenges. This paper details a new algorithm that explores the potential of using building boundaries for positioning and heading estimation. Rather than applying complex simulations to analyze and correct signal reflections by buildings, the approach utilizes a convolutional neural network to differentiate between the sky and building in a sky-pointing fisheye image. A new skymask matching algorithm is then proposed to match the segmented fisheye images with skymasks generated from a 3D building model. Each matched skymask holds a latitude, longitude coordinate and heading angle to determine the precise location of the fisheye image. The results are then compared with the smartphone GNSS and advanced 3DMA GNSS positioning methods. The proposed method provides degree-level heading accuracy, and improved positioning accuracy similar to other advanced 3DMA GNSS positioning methods in a rich urban environment.

Original languageEnglish
Article number4728
Pages (from-to)1-17
Number of pages17
JournalSensors (Switzerland)
Volume20
Issue number17
DOIs
Publication statusPublished - 1 Sep 2020

Keywords

  • Autonomous driving
  • Cameras
  • GNSS
  • GPS
  • Image segmentation
  • Land application
  • Localization
  • Navigation
  • Urban canyon

ASJC Scopus subject areas

  • Analytical Chemistry
  • Biochemistry
  • Atomic and Molecular Physics, and Optics
  • Instrumentation
  • Electrical and Electronic Engineering

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