Visual inertial odometry using coupled nonlinear optimization

Euntae Hong, Jongwoo Lim

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Scopus citations

Abstract

Visual inertial odometry (VIO) gained lots of interest recently for efficient and accurate ego-motion estimation of robots and automobiles. With a monocular camera and an inertial measurement unit (IMU) rigidly attached, VIO aims to estimate the 3D pose trajectory of the device in a global metric space. We propose a novel visual inertial odometry algorithm which directly optimizes the camera poses with noisy IMU data and visual feature locations. Instead of running separate filters for IMU and visual data, we put them into a unified non-linear optimization framework in which the perspective reprojection costs of visual features and the motion costs on the acceleration and angular velocity from the IMU and pose trajectory are jointly optimized. The proposed system is tested on the EuRoC dataset for quantitative comparison with the state-of-the-art in visual-inertial odometry and on the mobile phone data as a real-world application. The proposed algorithm is conceptually very clear and simple, achieves good accuracy, and can be easily implemented using publicly available non-linear optimization toolkits.

Original languageEnglish
Title of host publicationIROS 2017 - IEEE/RSJ International Conference on Intelligent Robots and Systems
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages6879-6885
Number of pages7
Volume2017-September
ISBN (Electronic)9781538626825
DOIs
StatePublished - 2017 Dec 13
Event2017 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2017 - Vancouver, Canada
Duration: 2017 Sep 242017 Sep 28

Other

Other2017 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2017
CountryCanada
CityVancouver
Period17/09/2417/09/28

Fingerprint Dive into the research topics of 'Visual inertial odometry using coupled nonlinear optimization'. Together they form a unique fingerprint.

  • Cite this

    Hong, E., & Lim, J. (2017). Visual inertial odometry using coupled nonlinear optimization. In IROS 2017 - IEEE/RSJ International Conference on Intelligent Robots and Systems (Vol. 2017-September, pp. 6879-6885). [8206610] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IROS.2017.8206610