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Keyframe-based stereo visual-inertial SLAM using nonlinear optimization
Author(s): Chang Chen; Lei Wang; Hua Zhu; Weiqi Lan
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Paper Abstract

Accuracy is highly important on autonomous robots. In this work, we propose a novel visual-inertial SLAM with stereo camera and IMU, which construct sparse map and estimate the camera poses accurately. The camera and IMU data are tightly coupled by nonlinear optimization. pre-integration is used to integrate rotation, velocity, and the pose matrix. A serious techniques are adapted to feature extraction, keyframe selection select keyframes, and loop closure. In addition, the system can run real-time on standard computer. The system localization accuracy can arrive centimetre-level especially in a large scale environment, and system is robust. We elevate the system on public datasets to compare other visual-inertial SLAM approaches; our system achieves better accuracy and robustness.

Paper Details

Date Published: 31 August 2018
PDF: 9 pages
Proc. SPIE 10835, Global Intelligence Industry Conference (GIIC 2018), 108350T (31 August 2018); doi: 10.1117/12.2503880
Show Author Affiliations
Chang Chen, China Univ. of Mining and Technology (China)
Jiangsu Collaborative Innovation Ctr. of Intelligent Mining Equipment (China)
Lei Wang, China Univ. of Mining and Technology (China)
Jiangsu Collaborative Innovation Ctr. of Intelligent Mining Equipment (China)
Hua Zhu, China Univ. of Mining and Technology (China)
Jiangsu Collaborative Innovation Ctr. of Intelligent Mining Equipment (China)
Weiqi Lan, China Univ. of Mining and Technology (China)


Published in SPIE Proceedings Vol. 10835:
Global Intelligence Industry Conference (GIIC 2018)
Yueguang Lv, Editor(s)

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