A UPF-UKF Framework For SLAM

Abstract

In this paper we propose a SLAM framework which is based on an algorithm that combines an unscented particle filter (UPF) and unscented Kalman filters (UKFs). A UPF is used to estimate robot's poses and the UKFs are used to represent landmark positions. UPF can estimate robot poses more consistently and accurately than generic particle filters (PFs), especially when models are highly non-linear or noises are not Gaussian. UKF can update landmarks more accurately compared to popular EKF's when highly non-linear observation models are used. In addition, our algorithm avoids the calculation of the Jacobian for both motion model and the observation model, which could be extremely difficult for high order systems. The calculation cost of a UPF is on the same order of magnitude as a particle filter (PF), which uses Kalman filters to generate proposal distributions, and the calculation cost of a UKF is equivalent to an EKF. As a result, our SLAM framework is more accurate than other popular SLAM frameworks while its efficiency is maintained. Simulation results are shown to validate the performance goals.