Simultaneous localization and mapping: A feature-based probabilistic approach
International Journal of Applied Mathematics and Computer Science - Special Section: Robot Control Theory Cezary Zielinski
On the nonlinear observability and the information form of the LAM problem
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
On the consistency of EKF-SLAM: focusing on the observation models
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Observability-based Rules for Designing Consistent EKF SLAM Estimators
International Journal of Robotics Research
Silent localization of underwater sensors using magnetometers
EURASIP Journal on Advances in Signal Processing - Special issue on advances in signal processing for maritime applications
Action selection for single-camera SLAM
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
| Hi-index | 0.00 |
This paper presents an analysis of the fully correlated approach to the simultaneous localization and map building (SLAM) problem from a control systems theory point of view, both for linear and nonlinear vehicle models. We show how partial observability hinders full reconstructibility of the state space, making the final map estimate dependent on the initial observations. Nevertheless, marginal filter stability guarantees convergence of the state error covariance to a positive semidefinite covariance matrix. By characterizing the form of the total Fisher information, we are able to determine the unobservable state space directions. Moreover, we give a closed-form expression that links the amount of reconstruction error to the number of landmarks used. The analysis allows the formulation of measurement models that make SLAM observable.