Next Article in Journal
A Novel Method for Remote Depth Estimation of Buried Radioactive Contamination
Next Article in Special Issue
Behavior Analysis of Novel Wearable Indoor Mapping System Based on 3D-SLAM
Previous Article in Journal
Optical Fiber Sensors Based on Fiber Ring Laser Demodulation Technology
Previous Article in Special Issue
Study of the Integration of the CNU-TS-1 Mobile Tunnel Monitoring System
Article Menu
Issue 2 (February) cover image

Export Article

Open AccessArticle
Sensors 2018, 18(2), 506; https://doi.org/10.3390/s18020506

Accurate Initial State Estimation in a Monocular Visual–Inertial SLAM System

School of Optics and Photonics, Beijing Institute of Technology, Beijing 100081, China
*
Author to whom correspondence should be addressed.
Received: 30 November 2017 / Revised: 31 January 2018 / Accepted: 3 February 2018 / Published: 8 February 2018
(This article belongs to the Special Issue Indoor LiDAR/Vision Systems)
View Full-Text   |   Download PDF [6821 KB, uploaded 8 February 2018]   |  

Abstract

The fusion of monocular visual and inertial cues has become popular in robotics, unmanned vehicles and augmented reality fields. Recent results have shown that optimization-based fusion strategies outperform filtering strategies. Robust state estimation is the core capability for optimization-based visual–inertial Simultaneous Localization and Mapping (SLAM) systems. As a result of the nonlinearity of visual–inertial systems, the performance heavily relies on the accuracy of initial values (visual scale, gravity, velocity and Inertial Measurement Unit (IMU) biases). Therefore, this paper aims to propose a more accurate initial state estimation method. On the basis of the known gravity magnitude, we propose an approach to refine the estimated gravity vector by optimizing the two-dimensional (2D) error state on its tangent space, then estimate the accelerometer bias separately, which is difficult to be distinguished under small rotation. Additionally, we propose an automatic termination criterion to determine when the initialization is successful. Once the initial state estimation converges, the initial estimated values are used to launch the nonlinear tightly coupled visual–inertial SLAM system. We have tested our approaches with the public EuRoC dataset. Experimental results show that the proposed methods can achieve good initial state estimation, the gravity refinement approach is able to efficiently speed up the convergence process of the estimated gravity vector, and the termination criterion performs well. View Full-Text
Keywords: visual–inertial SLAM; initial state estimation; termination criterion visual–inertial SLAM; initial state estimation; termination criterion
Figures

Graphical abstract

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Share & Cite This Article

MDPI and ACS Style

Mu, X.; Chen, J.; Zhou, Z.; Leng, Z.; Fan, L. Accurate Initial State Estimation in a Monocular Visual–Inertial SLAM System. Sensors 2018, 18, 506.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top