  In this article we investigate smoothing (i.e., optimisation-based)
estimation techniques for robot localization using an IMU aided by other
localization sensors. We more particularly focus on Invariant Smoothing (IS), a
variant based on the use of nontrivial Lie groups from robotics. We study the
recently introduced Two Frames Group (TFG), and prove it can fit into the
framework of Invariant Smoothing in order to better take into account the IMU
biases, as compared to the state-of-the-art in robotics. Experiments based on
the KITTI dataset show the proposed framework compares favorably to the
state-of-the-art smoothing methods in terms of robustness in some challenging
situations.
