Kalman Filter-based SLAM with Unknown Data Association using Symmetric Measurement Equations
Abstract:
This work investigates a novel method for dealing
with unknown data associations in Kalman filter-based
Simultaneous Localization and Mapping (SLAM) problems. The key
idea is to employ the concept of Symmetric Measurement
Equations (SMEs) in order to remove the data association
uncertainty from the original measurement equation. Based
on the resulting modified measurement equation, standard
nonlinear Kalman filters can estimate the full joint state vector
of the robot and landmarks without explicitly calculating data
association hypotheses.
with unknown data associations in Kalman filter-based
Simultaneous Localization and Mapping (SLAM) problems. The key
idea is to employ the concept of Symmetric Measurement
Equations (SMEs) in order to remove the data association
uncertainty from the original measurement equation. Based
on the resulting modified measurement equation, standard
nonlinear Kalman filters can estimate the full joint state vector
of the robot and landmarks without explicitly calculating data
association hypotheses.
| Zugehörige Institution(en) am KIT | Institut für Anthropomatik und Robotik (IAR) |
| Publikationstyp | Proceedingsbeitrag |
| Publikationsjahr | 2015 |
| Sprache | Englisch |
| Identifikator | ISBN: 978-1-4799-7772-7 KITopen-ID: 1000051039 |
| Erschienen in | Proceedings of the 2015 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2015), 14-16 Sept. 2015, San Diego, CA, USA |
| Verlag | Institute of Electrical and Electronics Engineers (IEEE) |
| Seiten | 49-53 |
| Nachgewiesen in | Dimensions |