Estimation with Applications to Tracking and Navigation
Estimation with Applications to Tracking and Navigation
A Discussion of Simultaneous Localization and Mapping
Autonomous Robots
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
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In this paper a new strategy for handling the observation information of a bearing-range sensor throughout the filtering process of EKF-SLAM is proposed. This new strategy is advised based on a thorough consistency analysis and aims to improve the process consistency while reducing the computational cost. At first, three different possible observation models are introduced for the EKF-SLAM solution for a robot equipped with a bearing-range sensor. General form of the covariance matrix and the level of inconsistency in the robot orientation estimate is then calculated for these variants, and based on the numerical comparison of the estimation results, it is proposed to use the bearing and range information of a feature in the initialization step of EKF-SLAM. However, it is recommended to use only the bearing information to perform other iteration steps. The simulation observations verify that the new strategy yields to more consistent estimates both for the robot and the features. Moreover, through the proposed consistency analysis, it is shown that since the source of consistency improvement is independent from the choice of the motion model, it gives us an advantage over other existing methods that assume a specific motion models for consistency improvement.