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.
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Hong, E.; Lim, J. Visual-Inertial Odometry with Robust Initialization and Online Scale Estimation. Sensors2018, 18, 4287.
Hong E, Lim J. Visual-Inertial Odometry with Robust Initialization and Online Scale Estimation. Sensors. 2018; 18(12):4287.
Hong, Euntae; Lim, Jongwoo. 2018. "Visual-Inertial Odometry with Robust Initialization and Online Scale Estimation." Sensors 18, no. 12: 4287.