Improved adaptive particle filter using adjusted variance and gradient data

Precise estimation of the position of robots, which is essential in mobile robotics, is difficult. However, particle filter shows great promise in such area. The number of samples is closely related to the operation time in particle filtering. The main issue in real-time situation with regard to particle filtering is to reduce the operation time, which led to the development of adaptive particle filter (APF). We propose a new APF, which adjusts the variance and then, uses the gradient data to generate samples near the high likelihood region. The simulation results show that the new APF performs better, in terms of the total operation time and sample set size, than the standard particle filter and the APF using Kullback-Leibler Distance (KLD) sampling.