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Advances in Robotics Research   Volume 2, Number 1, March 2018, pages 33-44
Depth-hybrid speeded-up robust features (DH-SURF) for real-time RGB-D SLAM
Donghwa Lee, Hyungjin Kim, Sungwook Jung and Hyun Myung

Abstract     [Full Text]
    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.
Key Words
    speeded-up robust feature (SURF); depth-hybrid; red-green-blue depth (RGB-D) sensor; simultaneous localization and mapping (SLAM)
Donghwa Lee: Division of Computer & Communication Engineering, Daegu University, Gyeongsan, Republic of Korea

Hyungjin Kim, Sungwook Jung and Hyun Myung: Urban Robotics Laboratory, Korea Advanced Institute of Science and Technology, 291 Daehak-ro, Yuseong-gu, Daejeon 34141, Republic of Korea

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