Combining KLD-sampling with Gmapping proposal for grid-based Monte Carlo localization of a moving robot

Particle filters using Gmapping proposal distribution has demonstrated their effectiveness in target tracking and robot self-localization. Due to the number of particles required in this approach, the computational demand is an issue associated with the Gmapping proposal distribution. The traditional approach is often ad hoc by setting a threshold for acceptance/rejection sampling to reduce the number of particles. However, the number of particles required in this approach is fixed and needs to be selected in advance which can be subjective and inefficient in representing a posterior distribution of various complexity. In parallel, the KLD-MCL algorithm has the capability to adaptively change the sample size of particles with an arbitrarily chosen proposal distribution. This paper combines the Gmapping proposal distribution with the KLD-MCL algorithm, resulting in an efficient particle filter which systematically adapts the number of particles. Simulation results demonstrate that the proposed approach has higher self-localization accuracy and requires a lower number of particles than the standard KLD-MCL algorithm.

[1]  A. Doucet,et al.  Maximum a Posteriori Sequence Estimation Using Monte Carlo Particle Filters , 2001, Annals of the Institute of Statistical Mathematics.

[2]  Wolfram Burgard,et al.  Improved Techniques for Grid Mapping With Rao-Blackwellized Particle Filters , 2007, IEEE Transactions on Robotics.

[3]  Thia Kirubarajan,et al.  Estimation with Applications to Tracking and Navigation: Theory, Algorithms and Software , 2001 .

[4]  Christian Musso,et al.  Improving Regularised Particle Filters , 2001, Sequential Monte Carlo Methods in Practice.

[5]  Branko Ristic,et al.  Beyond the Kalman Filter: Particle Filters for Tracking Applications , 2004 .

[6]  Simon J. Godsill,et al.  An Overview of Existing Methods and Recent Advances in Sequential Monte Carlo , 2007, Proceedings of the IEEE.

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

[8]  Dieter Fox,et al.  Adapting the Sample Size in Particle Filters Through KLD-Sampling , 2003, Int. J. Robotics Res..

[9]  Neil J. Gordon,et al.  A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking , 2002, IEEE Trans. Signal Process..

[10]  Wolfram Burgard,et al.  Monte Carlo localization for mobile robots , 1999, Proceedings 1999 IEEE International Conference on Robotics and Automation (Cat. No.99CH36288C).

[11]  N. Gordon,et al.  Novel approach to nonlinear/non-Gaussian Bayesian state estimation , 1993 .

[12]  M. Pitt,et al.  Filtering via Simulation: Auxiliary Particle Filters , 1999 .

[13]  David Portugal,et al.  An evaluation of 2D SLAM techniques available in Robot Operating System , 2013, 2013 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR).

[14]  Wolfram Burgard,et al.  Monte Carlo Localization: Efficient Position Estimation for Mobile Robots , 1999, AAAI/IAAI.

[15]  A. Doucet,et al.  A Tutorial on Particle Filtering and Smoothing: Fifteen years later , 2008 .