Visual SLAM in indoor environments using autonomous detection and registration of objects

Yong Ju Lee, Jae Bok Song

Research output: Contribution to conferencePaper

7 Citations (Scopus)

Abstract

For successful SLAM, landmarks for pose estimation should be continuously observed. This paper proposes autonomous detection of objects as visual landmarks for visual SLAM. Primitive features such as color and intensity, SIFT keypoints, and contour information are integrated to investigate environmental images and to distinguish objects from the background. Autonomous object detection can enable a robot to extract some objects without any prior information and it can help a vision system to cope with unknown environments. In addition, an adaptive weighting scheme and the use of a gradient of the gray scale are proposed to improve the performance of the proposed scheme. Using detected objects as landmarks, a robot can estimate its pose. A grid map of an unknown environment is built using an IR scanner and the detected objects are mapped in the grid map, which results in a hybrid grid/vision map. Visual SLAM using objects can have the less number of landmarks than other visual SLAM schemes using corners and lines. Various experiments show that the algorithm proposed in this paper can improve visual SLAM of a mobile robot.

Original languageEnglish
Pages671-676
Number of pages6
DOIs
Publication statusPublished - 2008
Event2008 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems, MFI - Seoul, Korea, Republic of
Duration: 2008 Aug 202008 Aug 22

Other

Other2008 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems, MFI
CountryKorea, Republic of
CitySeoul
Period08/8/2008/8/22

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Software
  • Computer Science Applications

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  • Cite this

    Lee, Y. J., & Song, J. B. (2008). Visual SLAM in indoor environments using autonomous detection and registration of objects. 671-676. Paper presented at 2008 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems, MFI, Seoul, Korea, Republic of. https://doi.org/10.1109/MFI.2008.4648022