  This paper introduces the united monocular-stereo features into a
visual-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-FUSION
from the following three perspectives. 1) UMS-VINS extracts and tracks features
from the sub-pixel plane to achieve better positions of the features. 2)
UMS-VINS introduces additional 2-dimensional features from the left and/or
right cameras. 3) If the visual initialization fails, the IMU propagation is
directly used for pose estimation, and if the visual-IMU alignment fails,
UMS-VINS estimates the pose via the visual odometry. The performances on both
public datasets and new real-world experiments indicate that the proposed
UMS-VINS outperforms the VINS-FUSION from the perspective of localization
accuracy, localization robustness, and environmental adaptability.
