Analysis and improvement of the consistency of extended Kalman filter based SLAM

Abstract— In this work, we study the inconsistency of EKFbased SLAM from the perspective of observability. We analytically prove that when the Jacobians of the state and measurement models are evaluated at the latest state estimates during every time step, the linearized error-state system model of the EKFbased SLAM has observable subspace of dimension higher than that of the actual, nonlinear, SLAM system. As a result, the covariance estimates of the EKF undergo reduction in directions of the state space where no information is available, which is a primary cause of inconsistency. To address this issue, a new “First Estimates Jacobian” (FEJ) EKF is proposed, which is shown to perform better in terms of consistency. In the FEJEKF, the filter Jacobians are calculated using the first-ever available estimates for each state variable, which insures that the observable subspace of the error-state system model is of the same dimension as that of the underlying nonlinear SLAM system. ...

• Type: Conference
• Year: 2008
• Where: ICRA
• Authors: Guoquan Huang, Anastasios I. Mourikis, Stergios I. Roumeliotis
• Keywords: EKFbased SLAM, ICRA 2008, Observable Subspace, Robotics, SLAM System