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Depth-hybrid speeded-up robust features (DH-SURF) for real-time RGB-D SLAM

  • Lee, Donghwa (Division of Computer & Communication Engineering, Daegu University) ;
  • Kim, Hyungjin (Urban Robotics Laboratory, Korea Advanced Institute of Science and Technology) ;
  • Jung, Sungwook (Urban Robotics Laboratory, Korea Advanced Institute of Science and Technology) ;
  • Myung, Hyun (Urban Robotics Laboratory, Korea Advanced Institute of Science and Technology)
  • Received : 2017.11.10
  • Accepted : 2017.12.22
  • Published : 2018.03.25

Abstract

This paper presents a novel feature detection algorithm called depth-hybrid speeded-up robust features (DH-SURF) augmented by depth information in the speeded-up robust features (SURF) algorithm. In the keypoint detection part of classical SURF, the standard deviation of the Gaussian kernel is varied for its scale-invariance property, resulting in increased computational complexity. We propose a keypoint detection method with less variation of the standard deviation by using depth data from a red-green-blue depth (RGB-D) sensor. Our approach maintains a scale-invariance property while reducing computation time. An RGB-D simultaneous localization and mapping (SLAM) system uses a feature extraction method and depth data concurrently; thus, the system is well-suited for showing the performance of the DH-SURF method. DH-SURF was implemented on a central processing unit (CPU) and a graphics processing unit (GPU), respectively, and was validated through the real-time RGB-D SLAM.

Acknowledgement

Grant : Development of robot intelligence technology for mobility with learning capability toward robust and seamless indoor and outdoor autonomous navigation

Supported by : Ministry of Trade, industry & Energy (MOTIE)

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