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A new visual/inertial integrated navigation algorithm based on sliding-window factor graph optimisation

Published online by Cambridge University Press:  01 December 2020

Haiying Liu ,
Jingqi Wang ,
Jianxin Feng  and
Xinyao Wang
Haiying Liu*
Affiliation:
College of Astronautics, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, P. R. China
Jingqi Wang
Affiliation:
College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, P. R. China
Jianxin Feng
Affiliation:
College of Astronautics, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, P. R. China
Xinyao Wang
Affiliation:
College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, P. R. China
*
*Corresponding author. E-mail: liuhaiying@nuaa.edu.cn
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Abstract

Visual–Inertial Navigation Systems (VINS) plays an important role in many navigation applications. In order to improve the performance of VINS, a new visual/inertial integrated navigation method, named Sliding-Window Factor Graph optimised algorithm with Dynamic prior information (DSWFG), is proposed. To bound computational complexity, the algorithm limits the scale of data operations through sliding windows, and constructs the states to be optimised in the window with factor graph; at the same time, the prior information for sliding windows is set dynamically to maintain interframe constraints and ensure the accuracy of the state estimation after optimisation. First, the dynamic model of vehicle and the observation equation of VINS are introduced. Next, as a contrast, an Invariant Extended Kalman Filter (InEKF) is constructed. Then, the DSWFG algorithm is described in detail. Finally, based on the test data, the comparison experiments of Extended Kalman Filter (EKF), InEKF and DSWFG algorithms in different motion scenes are presented. The results show that the new method can achieve superior accuracy and stability in almost all motion scenes.

Keywords

visual–inertial navigationInEKFfactor graph optimisationsliding-window filter

Information

Type
Research Article
Information
The Journal of Navigation , Volume 74 , Issue 2 , March 2021 , pp. 343 - 359
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Copyright
Copyright © The Royal Institute of Navigation 2020

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