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.

翻译：本文研究利用惯性测量单元（IMU）辅助其他定位传感器的机器人定位中的平滑（即基于优化的）估计技术。我们特别关注不变平滑（Invariant Smoothing, IS），这是一种基于机器人学中非平凡李群的变体方法。我们研究了近期提出的双框架群（Two Frames Group, TFG），并证明其可融入不变平滑框架，从而相较机器人学领域的现有技术能更好地考虑IMU偏置。基于KITTI数据集的实验表明，在部分具有挑战性的场景中，所提框架在鲁棒性方面优于现有先进平滑方法。