NDLI: A practical approach for EKF-SLAM in an indoor environment: fusing ultrasonic sensors and stereo camera

Content Provider	Springer Nature Link
Author	Ahn, SungHwan Choi, Jinwoo Doh, Nakju Lett Chung, Wan Kyun
Copyright Year	2008
Abstract	Improving the practical capability of SLAM requires effective sensor fusion to cope with the large uncertainties from the sensors and environment. Fusing ultrasonic and vision sensors possesses advantages of both economical efficiency and complementary cooperation. In particular, it can resolve the false data association and divergence problem of an ultrasonic sensor-only algorithm and overcome both the low frequency of SLAM update caused by the computational burden and the weakness to illumination changes of a vision sensor-only algorithm. In this paper, we propose a VR-SLAM (Vision and Range sensor-SLAM) algorithm to combine ultrasonic sensors and stereo camera very effectively. It consists of two schemes: (1) extracting robust point and line features from sonar data and (2) recognizing planar visual objects using a multi-scale Harris corner detector and its SIFT descriptor from a pre-constructed object database. We show that fusing these schemes through EKF-SLAM frameworks can achieve correct data association via the object recognition and high frequency update via the sonar features. The performance of the proposed algorithm was verified by experiments in various real indoor environments.
Starting Page	315
Ending Page	335
Page Count	21
File Format	PDF
ISSN	09295593
Journal	Autonomous Robots
Volume Number	24
Issue Number	3
e-ISSN	15737527
Language	English
Publisher	Springer US
Publisher Date	2008-01-05
Publisher Place	Boston
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Ultrasonic sensor Sonar feature detection Stereo camera Visual object recognition SLAM Mobile robot Simulation and Modeling Mechanical Engineering Computer Imaging, Vision, Pattern Recognition and Graphics Electronic and Computer Engineering Automation and Robotics Artificial Intelligence (incl. Robotics)
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence

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