Adaptive prior boosting technique for the efficient sample size in fastSLAM

Abstract

FastSLAM has been shown to degenerate over time due to sample impoverishment, that is, poor samples are generated during the sampling process. One of major culprits of the sample impoverishment problem is lack of the number of particles estimating the pose of the robot and the environment. In this work, an adaptive prior boosting technique is proposed for the efficient sample size according to the uncertainty of each situation in performing FastSLAM. It uses a back-propagation neural network, learned in various environments, in order to decide the required sample size. This adaptive approach generates a small number of particles when the uncertainty is low while performing FastSLAM, and it generates a large number of particles when the uncertainty is high. This technique efficiently generates the sample size in computer simulations and real environmental experiments, which significantly reduces the RMS feature and position errors.