Continuous Preintegration Theory for Graph basedvisual inertial Navigation

Abstract

Visual-inertial navigation that is able to provide accurate 3D localization in GPS-denied environments has seen popularity in recent years due to the proliferation of cost-effective cameras and inertial measurement units (IMUs). While an extended Kalman filter (EKF) is often used for sensor fusion, factor graph-based optimization has recently revealed its superior performance, which, however, is still compromised by the lack of rigorous IMU preintegration (i.e., integrating IMU measurements in a local frame of reference). To address this issue, in this paper, we analytically derive preintegration based on closed-form solutions of the continuous IMU measurement equations. These expressions allow us to analytically compute the mean, covariance, and bias Jacobians for a set of IMU preintegration factors. These accurate factors are subsequently fused with the visual information via visual-inertial factor graph optimization to provide high-precision trajectory estimates. The proposed method is validated on both Monte Carlo simulations and real-world experiments.

Guoquan Huang: This work was partially supported by the University of Delaware College of Engineering, UD Cybersecurity Initiative, the Delaware NASA/EPSCoR Seed Grant, the NSF (IIS-1566129), and the DTRA (HDTRA1-16-1-0039).

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Authors and Affiliations

1. Department of Mechanical Engineering, University of Delaware, Newark, DE, USA

   Kevin Eckenhoff, Patrick Geneva & Guoquan Huang

Corresponding author

Correspondence to Kevin Eckenhoff .

Editor information

Editors and Affiliations

1. Industrial Engineering and Operations Research, University of California, Berkeley, CA, USA

   Dr. Ken Goldberg

2. Electrical Engineering and Computer Sciences, University of California, Berkeley, CA, USA

   Pieter Abbeel

3. Rutgers University, Piscataway, NJ, USA

   Kostas Bekris

4. University of California, Berkeley, CA, USA

   Lauren Miller

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Eckenhoff, K., Geneva, P., Huang, G. (2020). High-Accuracy Preintegration for Visual-Inertial Navigation. In: Goldberg, K., Abbeel, P., Bekris, K., Miller, L. (eds) Algorithmic Foundations of Robotics XII. Springer Proceedings in Advanced Robotics, vol 13. Springer, Cham. https://doi.org/10.1007/978-3-030-43089-4_4

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• DOI : https://doi.org/10.1007/978-3-030-43089-4_4

• Published: 07 May 2020

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