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Micro Aerial Vehicle Navigation with Visual-Inertial Integration Aided by Structured Light

Published online by Cambridge University Press:  01 July 2019

Yunshu Wang ,
Jianye Liu ,
Jinling Wang ,
Qinghua Zeng ,
Xuesong Shen  and
Yueyuan Zhang
Yunshu Wang
Affiliation:
(Navigation Research Center, College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, 211106, China) (Jiangsu Key Laboratory of Internet of Things and Control Technologies (NUAA), Nanjing, 211106, China) (School of Civil and Environmental Engineering, University of New South Wales, Sydney, NSW 2052, Australia)
Jianye Liu
Affiliation:
(Navigation Research Center, College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, 211106, China) (Jiangsu Key Laboratory of Internet of Things and Control Technologies (NUAA), Nanjing, 211106, China)
Jinling Wang
Affiliation:
(School of Civil and Environmental Engineering, University of New South Wales, Sydney, NSW 2052, Australia)
Qinghua Zeng*
Affiliation:
(Navigation Research Center, College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, 211106, China) (Jiangsu Key Laboratory of Internet of Things and Control Technologies (NUAA), Nanjing, 211106, China)
Xuesong Shen
Affiliation:
(School of Civil and Environmental Engineering, University of New South Wales, Sydney, NSW 2052, Australia)
Yueyuan Zhang
Affiliation:
(Navigation Research Center, College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, 211106, China)
*
(E-mail: zengqh@nuaa.edu.cn)
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Abstract

Considering that traditional visual navigation cannot be utilised in low illumination and sparse feature environments, a novel visual-inertial integrated navigation method using a Structured Light Visual (SLV) sensor for Micro Aerial Vehicles (MAVs) is proposed in this paper. First, the measurement model based on an SLV sensor is studied and built. Then, using the state model based on error equations of an Inertial Navigation System (INS), the measurement model based on the error of the relative motion measured by INS and SLV is built. Considering that the measurements in this paper are mainly related to the position and attitude information of the present moment, the state error accumulation in traditional visual-inertial navigation can be avoided. An Adaptive Sage-Husa Kalman Filter (ASHKF) based on multiple weighting factors is proposed and designed to make full use of the SLV measurements. The results of the simulation and the experiment based on real flight data indicate that high accuracy position and attitude estimations can be obtained with the help of the algorithm proposed in this paper.

Keywords

Integrated Navigation System (INS)Indoor NavigationInertial Navigation System (INS)Optical NavigationLASER
Type
Research Article
Information
The Journal of Navigation , Volume 73 , Issue 1 , January 2020 , pp. 16 - 36
Copyright
Copyright © The Royal Institute of Navigation 2019 

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