Next Article in Journal
Validation of an Airborne Doppler Wind Lidar in Tropical Cyclones
Next Article in Special Issue
Aerial Coverage Analysis of Cellular Systems at LTE and mmWave Frequencies Using 3D City Models
Previous Article in Journal
Lift-off Effect for Capacitive Imaging Sensors
Previous Article in Special Issue
Visual-Based SLAM Configurations for Cooperative Multi-UAV Systems with a Lead Agent: An Observability-Based Approach
Article Menu

Export Article

Open AccessArticle
Sensors 2018, 18(12), 4287; https://doi.org/10.3390/s18124287

Visual-Inertial Odometry with Robust Initialization and Online Scale Estimation

Division of Computer Science and Engineering, Hanyang University, Seoul 133-791, Korea
*
Author to whom correspondence should be addressed.
Received: 26 September 2018 / Revised: 28 November 2018 / Accepted: 3 December 2018 / Published: 5 December 2018
(This article belongs to the Special Issue Unmanned Aerial Vehicle Networks, Systems and Applications)
Full-Text   |   PDF [3498 KB, uploaded 17 December 2018]   |  

Abstract

Visual-inertial odometry (VIO) has recently received much attention for efficient and accurate ego-motion estimation of unmanned aerial vehicle systems (UAVs). Recent studies have shown that optimization-based algorithms achieve typically high accuracy when given enough amount of information, but occasionally suffer from divergence when solving highly non-linear problems. Further, their performance significantly depends on the accuracy of the initialization of inertial measurement unit (IMU) parameters. In this paper, we propose a novel VIO algorithm of estimating the motional state of UAVs with high accuracy. The main technical contributions are the fusion of visual information and pre-integrated inertial measurements in a joint optimization framework and the stable initialization of scale and gravity using relative pose constraints. To account for the ambiguity and uncertainty of VIO initialization, a local scale parameter is adopted in the online optimization. Quantitative comparisons with the state-of-the-art algorithms on the European Robotics Challenge (EuRoC) dataset verify the efficacy and accuracy of the proposed method.
View Full-Text
Keywords: visual-inertial odometry; UAV navigation; sensor fusion; optimization visual-inertial odometry; UAV navigation; sensor fusion; optimization
Figures

Figure 1

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).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Hong, E.; Lim, J. Visual-Inertial Odometry with Robust Initialization and Online Scale Estimation. Sensors 2018, 18, 4287.

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