Journal
INDUSTRIAL ROBOT-THE INTERNATIONAL JOURNAL OF ROBOTICS RESEARCH AND APPLICATION
Volume 45, Issue 3, Pages 401-406Publisher
EMERALD GROUP PUBLISHING LTD
DOI: 10.1108/IR-01-2018-0002
Keywords
Mobile robots; Nonlinear optimization; Optical flow method; Tightly-coupled; Visual-inertial SLAM
Categories
Funding
- National 863 Program of China [2012AA041504]
- Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD)
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Purpose - This study aims to present a visual-inertial simultaneous localization and mapping (SLAM) method for accurate positioning and navigation of mobile robots in the event of global positioning system (GPS) signal failure in buildings, trees and other obstacles. Design/methodology/approach - In this framework, a feature extraction method distributes features on the image under texture-less scenes. The assumption of constant luminosity is improved, and the features are tracked by the optical flow to enhance the stability of the system. The camera data and inertial measurement unit data are tightly coupled to estimate the pose by nonlinear optimization. Findings - The method is successfully performed on the mobile robot and steadily extracts the features on low texture environments and tracks features. The end-to-end error is 1.375 m with respect to the total length of 762 m. The authors achieve better relative pose error, scale and CPU load than ORB-SLAM2 on EuRoC data sets. Originality/value - The main contribution of this study is the theoretical derivation and experimental application of a new visual-inertial SLAM method that has excellent accuracy and stability on weak texture scenes.
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