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