Particle Filter SLAM with High Dimensional Vehicle Model
2008 (English)Report (Other academic)
This work presents a particle filter (PF) method closely related to FastSLAM for solving the simultaneous localization and mapping (SLAM) problem. Using the standard FastSLAM algorithm, only low-dimensional vehicle models can be handled due to computational constraints. In this work an extra factorization of the problem is introduced that makes high-dimensional vehicle models computationally feasible. Results using experimental data from a UAV (helicopter) are presented. The algorithm fuses measurements from on-board inertial sensors (accelerometer and gyro), barometer, and vision in order to solve the SLAM problem.
Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2008. , 21 p.
LiTH-ISY-R, ISSN 1400-3902 ; 2863
Rao-Blackwellized/marginalized particle lter, sensor fusion, simultaneous localization and mapping, inertial sensors, UAV, vision.
IdentifiersURN: urn:nbn:se:liu:diva-56186ISRN: LiTH-ISY-R-2863OAI: oai:DiVA.org:liu-56186DiVA: diva2:316985