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
An Experimental Study of a Cooperative Positioning System
Autonomous Robots
A Probabilistic Approach to Collaborative Multi-Robot Localization
Autonomous Robots
Distributed maximum a posteriori estimation for multi-robot cooperative localization
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Consistent cooperative localization
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Observability-based Rules for Designing Consistent EKF SLAM Estimators
International Journal of Robotics Research
Interrobot transformations in 3-D
IEEE Transactions on Robotics
Deterministic robot-network localization is hard
IEEE Transactions on Robotics
Performance analysis of multirobot Cooperative localization
IEEE Transactions on Robotics
Optimal sensor scheduling for resource-constrained localization of mobile robot formations
IEEE Transactions on Robotics
Convergence and Consistency Analysis for Extended Kalman Filter Based SLAM
IEEE Transactions on Robotics
Robot-to-Robot Relative Pose Estimation From Range Measurements
IEEE Transactions on Robotics
Evolutionary computation for intelligent self-localization in multiple mobile robots based on SLAM
ICIRA'12 Proceedings of the 5th international conference on Intelligent Robotics and Applications - Volume Part I
Camera-IMU-based localization: Observability analysis and consistency improvement
International Journal of Robotics Research
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In this paper, we investigate the consistency of extended Kalman filter (EKF)-based cooperative localization (CL) from the perspective of observability. We analytically show that the error-state system model employed in the standard EKF-based CL always has an observable subspace of higher dimension than that of the actual nonlinear CL system. This results in unjustified reduction of the EKF covariance estimates in directions of the state space where no information is available, and thus leads to inconsistency. To address this problem, we adopt an observability-based methodology for designing consistent estimators in which the linearization points are selected to ensure a linearized system model with observable subspace of correct dimension. In particular, we propose two novel observability-constrained (OC)-EKF estimators that are instances of this paradigm. In the first, termed OC-EKF 1.0, the filter Jacobians are calculated using the prior state estimates as the linearization points. In the second, termed OC-EKF 2.0, 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. The proposed OC-EKFs have been tested in simulation and experimentally, and have been shown to significantly outperform the standard EKF in terms of both accuracy and consistency.