Doorway detection for autonomous indoor navigation of unmanned vehicles

Burak Kakillioglu, Koray Ozcan, Senem Velipasalar

Research output: Chapter in Book/Report/Conference proceedingConference contribution

7 Scopus citations

Abstract

Fully autonomous navigation of unmanned vehicles, without relying on pre-installed tags or markers, still remains a challenge for GPS-denied areas and complex indoor environments. Doors are important for navigation as the entry/exit points. A novel approach is proposed to autonomously detect™ doorways by using the Project Tango platform. We first detect the candidate door openings from the 3D point cloud, and then use a pre-trained detector on corresponding RGB image regions to verify if these openings are indeed doors. We employ Aggregate Channel Features for detection, which are computationally efficient for real-time applications. Since detection is only performed on candidate regions, the system is more robust against false positives. The approach can be generalized to recognize windows, some architectural structures and obstacles. Experiments show that the proposed method can detect open doors in a robust and efficient manner.

Original languageEnglish (US)
Title of host publication2016 IEEE International Conference on Image Processing, ICIP 2016 - Proceedings
PublisherIEEE Computer Society
Pages3837-3841
Number of pages5
Volume2016-August
ISBN (Electronic)9781467399616
DOIs
StatePublished - Aug 3 2016
Event23rd IEEE International Conference on Image Processing, ICIP 2016 - Phoenix, United States
Duration: Sep 25 2016Sep 28 2016

Other

Other23rd IEEE International Conference on Image Processing, ICIP 2016
CountryUnited States
CityPhoenix
Period9/25/169/28/16

Keywords

  • Aggregate channel features
  • Depth data
  • Door detection
  • Indoor navigation

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Signal Processing

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  • Cite this

    Kakillioglu, B., Ozcan, K., & Velipasalar, S. (2016). Doorway detection for autonomous indoor navigation of unmanned vehicles. In 2016 IEEE International Conference on Image Processing, ICIP 2016 - Proceedings (Vol. 2016-August, pp. 3837-3841). [7533078] IEEE Computer Society. https://doi.org/10.1109/ICIP.2016.7533078