On achievable accuracy for pose tracking

Authors:
Andrea Censi
Affiliations:
Control & Dynamical Systems department, Division of Engineering and Applied Science, California Institute of Technology, Pasadena, CA
Venue:
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Year:
2009

Citing 5
Cited 0

Some characteristic properties of the Fisher information matrix via Cacoullos-type inequalities

Journal of Multivariate Analysis
A Discussion of Simultaneous Localization and Mapping

Autonomous Robots
Bayesian Bounds for Parameter Estimation and Nonlinear Filtering/Tracking

Bayesian Bounds for Parameter Estimation and Nonlinear Filtering/Tracking
Subjective local maps for hybrid metric-topological SLAM

Robotics and Autonomous Systems
Exactly Sparse Delayed-State Filters for View-Based SLAM

IEEE Transactions on Robotics

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper presents Cramér-Rao bound-like inequalities for pose tracking, which is defined as the problem of recovering the robot displacement given two successive readings of a relative sensor. Computing the exact Fisher Information Matrix (FIM) for pose tracking is hard, because the state comprises the map, which is infinite-dimensional and unknown. This paper shows that the FIM for pose tracking can be bounded by a function of the FIM for localization on a known map, thereby reducing the analysis to a finite-dimensional problem. The resulting bounds are independent of the map prior and representation. The results are valid for any relative sensor; the experimental verification is done for the particular case of pose tracking using range-finders (scan matching).