On the representation and estimation of spatial uncertainly
International Journal of Robotics Research
Elements of information theory
Elements of information theory
Matrix analysis and applied linear algebra
Matrix analysis and applied linear algebra
Estimation with Applications to Tracking and Navigation
Estimation with Applications to Tracking and Navigation
Fastslam: a factored solution to the simultaneous localization and mapping problem with unknown data association
Robocentric map joining: Improving the consistency of EKF-SLAM
Robotics and Autonomous Systems
The Effects of Partial Observability When Building Fully Correlated Maps
IEEE Transactions on Robotics
Convergence and Consistency Analysis for Extended Kalman Filter Based SLAM
IEEE Transactions on Robotics
Power-SLAM: a linear-complexity, anytime algorithm for SLAM
International Journal of Robotics Research
High-precision, consistent EKF-based visual-inertial odometry
International Journal of Robotics Research
Camera-IMU-based localization: Observability analysis and consistency improvement
International Journal of Robotics Research
Hi-index | 0.00 |
In this work, we study the inconsistency problem of extended Kalman filter (EKF)-based simultaneous localization and mapping (SLAM) from the perspective of observability. We analytically prove that when the Jacobians of the process and measurement models are evaluated at the latest state estimates during every time step, the linearized error-state system employed in the EKF has an observable subspace of dimension higher than that of the actual, non-linear, SLAM system. As a result, the covariance estimates of the EKF undergo reduction in directions of the state space where no information is available, which is a primary cause of the inconsistency. Based on these theoretical results, we propose a general framework for improving the consistency of EKF-based SLAM. In this framework, the EKF linearization points are selected in a way that ensures that the resulting linearized system model has an observable subspace of appropriate dimension. We describe two algorithms that are instances of this paradigm. In the first, termed observability constrained (OC)-EKF, the linearization points are selected so as to minimize their expected errors (i.e. the difference between the linearization point and the true state) under the observability constraints. In the second, the filter Jacobians are calculated using the first-ever available estimates for all state variables. This latter approach is termed first-estimates Jacobian (FEJ)-EKF. The proposed algorithms have been tested both in simulation and experimentally, and are shown to significantly outperform the standard EKF both in terms of accuracy and consistency.