Iterated filters for bearing-only SLAM

— This paper discusses the importance of iteration when performing the measurement update step for the problem of bearing-only SLAM. We focus on an undelayed approach that initializes a landmark after only one bearing measurement. Traditionally, the extended Kalman filter (EKF) has been used for SLAM, but the EKF measurement update rule can often lead to a divergent state estimate due to its inconsistency in linearization. We discuss the flaws of the EKF in this paper, and show that even the well established inverse-depth parametrization for bearing-only SLAM can be affected. We then show that representing the bearing-only update as a numerical optimization problem (solved with an iterative approach such as Gauss-Newton minimization) prevents divergence of the Kalman filter state and produces accurate SLAM results for a bearing-only sensor. More specifically, we propose the use of an iterated Kalman filter to resolve the issues normally associated with the EKF measurement update...

• Type: Conference
• Year: 2008
• Where: ICRA
• Authors: Stephen Tully, Hyungpil Moon, George Kantor, Howie Choset
• Keywords: Bearing-only Slam, EKF Measurement Update, ICRA 2008, Kalman filter, Robotics