Map-based Visual-Inertial Localization: A Numerical Study

We revisit the problem of efficiently leveraging prior map information within a visual-inertial estimation framework. The use of traditional landmark-based maps with 2D-to-3D measurements along with the recently introduced keyframe-based maps with 2D-to-2D measurements are investigated. The full joint estimation of the prior map is compared within a visual-inertial simulator to the Schmidt-Kalman filter (SKF) and measurement inflation methods in terms of their computational complexity, consistency, accuracy, and memory usage. This study shows that the SKF can enable efficient and consistent estimation for small workspace scenarios and the use of 2D-to-3D landmark maps have the highest levels of accuracy. Keyframe-based 2D-to-2D maps can reduce the required state size while still enabling accuracy gains. Finally, we show that measurement inflation methods, after tuning, can be accurate and efficient for large-scale environments if the guarantee of consistency is relaxed.