Preprint Article Version 1 This version is not peer-reviewed

Visual Inertial Odometry with Robust Initialization and Online Scale Estimation

Version 1 : Received: 26 September 2018 / Approved: 26 September 2018 / Online: 26 September 2018 (13:23:48 CEST)

A peer-reviewed article of this Preprint also exists.

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

Journal reference: Sensors 2018, 18, 4287
DOI: 10.3390/s18124287

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 count for 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 EuRoC dataset verify the efficacy and accuracy of the proposed method.

Subject Areas

visual-inertial odometry; UAV navigation; sensor fusion; optimization

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