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This paper introduces the united monocular-stereo features into avisual-inertial tightly coupled odometry (UMS-VINS) for robust pose estimation.UMS-VINS requires two cameras and a low-cost inertial measurement unit (IMU).The UMS-VINS is an evolution of VINS-FUSION, which modifies the VINS-FUSIONfrom the following three perspectives. 1) UMS-VINS extracts and tracks featuresfrom the sub-pixel plane to achieve better positions of the features. 2)UMS-VINS introduces additional 2-dimensional features from the left and/orright cameras. 3) If the visual initialization fails, the IMU propagation isdirectly used for pose estimation, and if the visual-IMU alignment fails,UMS-VINS estimates the pose via the visual odometry. The performances on bothpublic datasets and new real-world experiments indicate that the proposedUMS-VINS outperforms the VINS-FUSION from the perspective of localizationaccuracy, localization robustness, and environmental adaptability.

UMS-VINS: United Monocular-Stereo Features for Visual-Inertial Tightly Coupled Odometry