Consistency of the EKF-SLAM Algorithm

By Tim Bailey, Juan I. Nieto, José E. Guivant, Michael Stevens, Eduardo Mario Nebot. 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2006

630 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Convergence and Consistency Analysis for Extended Kalman Filter Based SLAM (2007, methodology): “However, the convergence proofs given in [2] is only for linear case and it has been shown recently by a number of researchers that EKF SLAM can produce inconsistent (overconfident) estimations [7], [8], [9], [10], [11].”
6D SLAM—3D mapping outdoor environments (2007, background): “Recently, on the theoretical side of SLAM, Bailey, Nieto, Guivant, Stevens & Nebot, 2006a proves that EKF-SLAM fails in large environments and FastSLAM is inconsistent as a statistical filter: It always underestimates its own error in the medium to long-term Bailey, Nieto & Nebot, 2006b.”
Simultaneous Localization and Mapping: A Survey of Current Trends in Autonomous Driving (2017, neutral): “It has been observed by multiple authors [111].”
A Bayesian exploration-exploitation approach for optimal online sensing and planning with a visually guided mobile robot (2009, background): “Path planning also plays a key role in the theoretical and practical convergence of SLAM algorithms [Bailey et al (2006); Martinez-Cantin et al (2006)].”
Indoor Tracking: Theory, Methods, and Technologies (2015, methodology): “For example, the solutions based on the EKF typically suffer from linearization errors [71], [72], whereas the solutions based on the PF suffer from particle depletion [73].”