Particle Filter SLAM with High Dimensional Vehicle Model
2009 (English)In: Journal of Intelligent and Robotic Systems, ISSN 0921-0296, E-ISSN 1573-0409, Vol. 55, no 4, 249-266 p.Article in journal (Refereed) Published
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
Springer Netherlands, 2009. Vol. 55, no 4, 249-266 p.
Rao-Blackwellized/marginalized particle filter, Sensor fusion, Simultaneous localization and mapping, Inertial sensors, UAV, Vision
IdentifiersURN: urn:nbn:se:liu:diva-15500DOI: 10.1007/s10846-008-9301-yOAI: oai:DiVA.org:liu-15500DiVA: diva2:117417