查看文章

Error-state extended Kalman filter (ESEKF) is one of the extensively used filtering techniques in robot systems. There are many works that cast ESEKF on manifolds to improve consistency. However, most of these works are designed case by case, which makes it difficult to extend to new manifolds. In this article, we propose a generic method to formulate the iterated error-state extended Kalman filter (IESEKF) on manifolds, which aims to facilitate the deployment of IESEKF for on-manifold systems (e.g., lidar-inertial and visual-inertial systems). First, a canonical on-manifold representation of the robot system is proposed, based on which, an on-manifold IESEKF framework is formulated and solved by linearization at each estimation point. The proposed framework has two main advantages, one is that an equivalent error-state system is derived from linearization, which is minimally parameterized without any …