ICE-BA: Incremental, Consistent and Efficient Bundle Adjustment for
Visual-Inertial SLAM
Abstract
Modern visual-inertial SLAM (VI-SLAM) achieves
higher accuracy and robustness than pure visual SLAM,
thanks to the complementariness of visual features and inertial measurements. However, jointly using visual and inertial measurements to optimize SLAM objective functions
is a problem of high computational complexity. In many VISLAM applications, the conventional optimization solvers
can only use a very limited number of recent measurements for real time pose estimation, at the cost of suboptimal localization accuracy. In this work, we renovate the
numerical solver for VI-SLAM. Compared to conventional
solvers, our proposal provides an exact solution with significantly higher computational efficiency. Our solver allows us to use remarkably larger number of measurements
to achieve higher accuracy and robustness. Furthermore,
our method resolves the global consistency problem that
is unaddressed by many state-of-the-art SLAM systems: to
guarantee the minimization of re-projection function and inertial constraint function during loop closure. Experiments
demonstrate our novel formulation renders lower localization error and more than 10x speedup compared to alternatives. We release the source code of our implementation to
benefit the community