Improving Monocular Visual-Inertial Initialization with Structureless Visual-Inertial Bundle Adjustment

Monocular visual inertial odometry (VIO) has facilitated a wide range of
real-time motion tracking applications, thanks to the small size of the sensor
suite and low power consumption. To successfully bootstrap VIO algorithms, the
initialization module is extremely important. Most initialization methods rely
on the reconstruction of 3D visual point clouds. These methods suffer from high
computational cost as state vector contains both motion states and 3D feature
points. To address this issue, some researchers recently proposed a
structureless initialization method, which can solve the initial state without
recovering 3D structure. However, this method potentially compromises
performance due to the decoupled estimation of rotation and translation, as
well as linear constraints. To improve its accuracy, we propose novel
structureless visual-inertial bundle adjustment to further refine previous
structureless solution. Extensive experiments on real-world datasets show our
method significantly improves the VIO initialization accuracy, while
maintaining real-time performance.