Rao-Blackwellized visual SLAM for small UAVs with vehicle model partition
Abstract
Purpose
The purpose of this paper is to present a Rao–Blackwellized particle filter (RBPF) approach for the visual simultaneous localization and mapping (SLAM) of small unmanned aerial vehicles (UAVs).
Design/methodology/approach
Measurements from inertial measurement unit, barometric altimeter and monocular camera are fused to estimate the state of the vehicle while building a feature map. In this SLAM framework, an extra factorization method is proposed to partition the vehicle model into subspaces as the internal and external states. The internal state is estimated by an extended Kalman filter (EKF). A particle filter is employed for the external state estimation and parallel EKFs are for the map management.
Findings
Simulation results indicate that the proposed approach is more stable and accurate than other existing marginalized particle filter-based SLAM algorithms. Experiments are also carried out to verify the effectiveness of this SLAM method by comparing with a referential global positioning system/inertial navigation system.
Originality/value
The main contribution of this paper is the theoretical derivation and experimental application of the Rao–Blackwellized visual SLAM algorithm with vehicle model partition for small UAVs.
Keywords
Acknowledgements
This work was supported by the National High-Tech Research & Development Program of China (Grant No. 2011AA040202) and National Science Fund of China (Grant No. 51005008). The authors would like to thank Yi Zhou, Chenghao Xue, Han Gao and Qingru Zeng for their great help during the experiments.
Citation
Wang, T., Wang, C., Liang, J. and Zhang, Y. (2014), "Rao-Blackwellized visual SLAM for small UAVs with vehicle model partition", Industrial Robot, Vol. 41 No. 3, pp. 266-274. https://doi.org/10.1108/IR-07-2013-378
Publisher
:Emerald Group Publishing Limited
Copyright © 2014, Emerald Group Publishing Limited