Keyframe-based stereo visual-inertial SLAM using nonlinear optimization

Chang Chen; Lei Wang; Hua Zhu; Weiqi Lan

doi:10.1117/12.2503880

31 August 2018 Keyframe-based stereo visual-inertial SLAM using nonlinear optimization

Chang Chen, Lei Wang, Hua Zhu, Weiqi Lan

Author Affiliations +

Proceedings Volume 10835, Global Intelligence Industry Conference (GIIC 2018); 108350T (2018) https://doi.org/10.1117/12.2503880
Event: Global Intelligent Industry Conference 2018, 2018, Beijing, China

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.

Citation Download Citation

Chang Chen, Lei Wang, Hua Zhu, and Weiqi Lan "Keyframe-based stereo visual-inertial SLAM using nonlinear optimization", Proc. SPIE 10835, Global Intelligence Industry Conference (GIIC 2018), 108350T (31 August 2018); https://doi.org/10.1117/12.2503880

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