Localization and map building based on particle filter and unscented Kalman Filter for an AUV

Simultaneous localization and mapping (SLAM) is of prime importance for navigation problem of autonomous underwater vehicle. Currently EKF-based SLAM and particle filter-based SLAM are prevalent methods though they have their own deficiency respectively. In this paper a modified RBPF method is proposed to apply in navigation and localization for our underwater vehicle, C-RANGER. Unscented Kalman filter instead of extended Kalman filter is used to incorporate the current observations as well as the historical observations into the proposal distribution. The simulation results show that the improved algorithm is more accurate and reliable while it is used to estimate the pose of AUV and locations of features.

[1]  G. Casella,et al.  Rao-Blackwellisation of sampling schemes , 1996 .

[2]  Eduardo Mario Nebot,et al.  Consistency of the FastSLAM algorithm , 2006, Proceedings 2006 IEEE International Conference on Robotics and Automation, 2006. ICRA 2006..

[3]  Sebastian Thrun,et al.  FastSLAM 2.0: An Improved Particle Filtering Algorithm for Simultaneous Localization and Mapping that Provably Converges , 2003, IJCAI.

[4]  Nando de Freitas,et al.  An Introduction to Sequential Monte Carlo Methods , 2001, Sequential Monte Carlo Methods in Practice.

[5]  Simon J. Godsill,et al.  On sequential Monte Carlo sampling methods for Bayesian filtering , 2000, Stat. Comput..

[6]  Hugh F. Durrant-Whyte,et al.  A solution to the simultaneous localization and map building (SLAM) problem , 2001, IEEE Trans. Robotics Autom..

[7]  Simon J. Godsill,et al.  On sequential simulation-based methods for Bayesian filtering , 1998 .

[8]  Sebastian Thrun,et al.  FastSLAM: a factored solution to the simultaneous localization and mapping problem , 2002, AAAI/IAAI.

[9]  Nando de Freitas,et al.  The Unscented Particle Filter , 2000, NIPS.

[10]  Hugh F. Durrant-Whyte,et al.  Simultaneous localization and mapping: part I , 2006, IEEE Robotics & Automation Magazine.

[11]  Rudolph van der Merwe,et al.  The unscented Kalman filter for nonlinear estimation , 2000, Proceedings of the IEEE 2000 Adaptive Systems for Signal Processing, Communications, and Control Symposium (Cat. No.00EX373).