Abstract
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.
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