Chapter
Aug 28, 2019
International Conference on Transportation and Development 2019

Landmark Assisted Stereo Visual Odometry

Publication: International Conference on Transportation and Development 2019: Innovation and Sustainability in Smart Mobility and Smart Cities

ABSTRACT

In this paper, we consider the positioning technology to facilitate the system services for automatic guided vehicle. Typically, quick response (QR) code is a feasible solution with high accuracy. However, the position information of QR code is discrete and sparse, which leads to uncertainty in position interpolation. To solve this problem, we proposed a positioning system of feature-based stereo visual odometry (VO) by combining some discrete and sparse absolute positioning information. In the proposed system, we adopt stereo cameras to obtain the depth of feature points directly and then apply an algorithm with two parallel threads of tracking and mapping. The mapping thread deals with not only information sent by tracking thread but also those sent by QR code. This proposed system has been tested and verified on our automatic guided vehicle. The measured and computational results suggest that the proposed system has acceptable robustness and relatively high accuracy.

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ACKNOWLEDGEMENTS

This work is supported by the Shenzhen Municipal Science and Technology Innovation Committee under Grant No. JCYJ20170412171044606.

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Go to International Conference on Transportation and Development 2019
International Conference on Transportation and Development 2019: Innovation and Sustainability in Smart Mobility and Smart Cities
Pages: 46 - 53
Editor: David A. Noyce, Ph.D., University of Wisconsin–Madison
ISBN (Online): 978-0-7844-8258-2

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Published online: Aug 28, 2019

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Division of Information Science and Technology, Graduate School at Shenzhen, Tsinghua Univ. 518055, Tsinghua Campus, The University Town, Shenzhen, Guangdong, China. E-mail: [email protected]
Division of Logistics and Transportation, Graduate School at Shenzhen, Tsinghua Univ. 518055, Tsinghua Campus, The University Town, Shenzhen, Guangdong, China. E-mail: [email protected]
Division of Logistics and Transportation, Graduate School at Shenzhen, Tsinghua Univ. 518055, Tsinghua Campus, The University Town, Shenzhen, Guangdong, China. E-mail: [email protected]
Division of Information Science and Technology, Graduate School at Shenzhen, Tsinghua Univ. 518055, Tsinghua Campus, The University Town, Shenzhen, Guangdong, China. E-mail: [email protected]

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