KLD sampling with Gmapping proposal for Monte Carlo localization of mobile robots

The paper proposes an algorithm for mobile robot navigation that integrates the Gmapping proposal distribution with the Kullback-Leibler divergence for adapting the number of particles. This results in a very effective particle filter with adaptive sample size. The algorithm has been evaluated in both simulation and experimental studies, using the standard KLD-sampling MCL as a benchmark. Simulation results show that the proposed algorithm achieves higher localization accuracy with a smaller number of particles compared to the benchmark algorithm. In a more realistic scenario using experimental data and simulated robot odometry with drift, the proposed algorithm again has greater accuracy using a lower number of particles.