Estimation with Applications to Tracking and Navigation
Estimation with Applications to Tracking and Navigation
Real-time and robust monocular SLAM using predictive multi-resolution descriptors
ISVC'06 Proceedings of the Second international conference on Advances in Visual Computing - Volume Part II
Convergence and Consistency Analysis for Extended Kalman Filter Based SLAM
IEEE Transactions on Robotics
Hi-index | 0.00 |
This paper addresses two key limitations of the unscented Kalman filter (UKF) when applied to the simultaneous localization and mapping (SLAM) problem: the cubic, in the number of states, computational complexity, and the inconsistency of the state estimates. In particular, we introduce a new sampling strategy that minimizes the linearization error and whose computational complexity is constant (i.e., independent of the size of the state vector). As a result, the overall computational complexity of UKF-based SLAM becomes of the same order as that of the extended Kalman filter (EKF) when applied to SLAM. Furthermore, we investigate the observability properties of the linear-regression-based model employed by the UKF, and propose a new algorithm, termed the Observability-Constrained (OC)-UKF, that improves the consistency of the state estimates. The superior performance of the OC-UKF compared to the standard UKF and its robustness to large linearization errors are validated by extensive simulations.