Abstract
Self-localization is a basic skill for mobile robots in the dynamic environments. It is usually modeled as a state estimation problem for nonlinear system with non-Gaussian noise and needs the real-time processing. Unscented particle filter (UPF) can handle the state estimation problem for nonlinear system with non-Gaussian noise; however the computation of UPF is very high. In order to reduce the computation cost of UPF and meanwhile maintain the accuracy, we propose an adaptive unscented particle filter (AUPF) algorithm through relative entropy. AUPF can adaptively adjust the number of particles during filtering to reduce the necessary computation and hence improve the real-time capability of UPF. In AUPF, the relative entropy is used to measure the distance between the empirical distribution and the true posterior distribution. The least number of particles for the next step is then decided according to the relative entropy. In order to offset the difference between the proposal distribution, and the true distribution the least number is adjusted thereafter. The ideal performance of AUPF in real robot self-localization is demonstrated.
Highlights
Autonomous mobile robots have increasingly been used in a wide range of applications [1, 2]
In the first experiment the performance of adaptive unscented particle filter (AUPF) is compared with Unscented particle filter (UPF) and particle filter (PF) through a simulation to validate the effectiveness of AUPF
The utilization and performance of AUPF for a real robot self-localization are illustrated in detail
Summary
Autonomous mobile robots have increasingly been used in a wide range of applications [1, 2]. The sensor-based method only uses the sensor information to realize the mobile robot self-localization. Bayesian filter is the most important probabilistic method for self-localization which is a state estimation problem for nonlinear system with non-Gaussian noise [8, 9]. EKF only uses the first-order term of the Taylor series expansions of the nonlinear functions This linearization often induces large errors in the estimated statistics of the posterior distributions. In traditional UPF the number of particles is fixed, and in each step each particle utilizes UKF to obtain the new importance proposal that can bring huge computation and influence the realtime capability of the filter. In this paper an adaptive unscented particle filter algorithm through relative entropy (AUPF) is proposed to enhance the real-time capability of UPF and ensure its accuracy.
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