Dynamic observers as asymptotic limits of recursive filters: special cases
SIAM Journal on Applied Mathematics
Optimal control: linear quadratic methods
Optimal control: linear quadratic methods
Estimating uncertain spatial relationships in robotics
Autonomous robot vehicles
A Probabilistic Approach to Concurrent Mapping and Localization for Mobile Robots
Machine Learning - Special issue on learning in autonomous robots
Robocentric map joining: Improving the consistency of EKF-SLAM
Robotics and Autonomous Systems
Exactly Sparse Extended Information Filters for Feature-based SLAM
International Journal of Robotics Research
Exploiting geometry for improved hybrid AOA/TDOA-based localization
Signal Processing
Hierarchical SLAM: Real-Time Accurate Mapping of Large Environments
IEEE Transactions on Robotics
Convergence and Consistency Analysis for Extended Kalman Filter Based SLAM
IEEE Transactions on Robotics
Hi-index | 0.01 |
This paper provides a novel solution for robocentric mapping using an autonomous mobile robot. The robot dynamic model is the standard unicycle model and the robot is assumed to measure both the range and relative bearing to the landmarks. The algorithm introduced in this paper relies on a coordinate transformation and an extended Kalman filter like algorithm. The coordinate transformation considered in this paper has not been previously considered for robocentric mapping applications. Moreover, we provide a rigorous stochastic stability analysis of the filter employed and we examine the conditions under which the mean-square estimation error converges to a steady-state value.