Redesign and analysis of globally asymptotically stable bearing only SLAM

The Simultaneous Localization And Mapping (SLAM) estimation problem is a nonlinear problem, due to the nature of the range and bearing measurements. In latter years it has been demonstrated that if the nonlinearities from the attitude are handled by a separate nonlinear observer, the SLAM dynamics can be represented as a linear time varying (LTV) system, by introducing these nonlinearities and nonlinear measurements as time varying vectors and matrices. This makes the SLAM estimation problem globally solvable with a Kalman filter, however, the noise structure is no longer trivial. In this paper, a new bearings only SLAM estimation algorithm is presented, including a novel design of the noise covariance matrices. Simulations of the SLAM estimator are presented, and show the performance of the state and uncertainty estimates, as well as the stability of the proposed estimator.