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.

In this paper, we present a delayed algorithm for landmark initialization based on a scaled unscented filter. The algorithm efficiently gives well-conditioned feature locations close to the particle filter based methods even for a very high dimensional nonlinear observation model. Comparing with EKF based methods, our method has the same computational cost while the calculation of Jacobian matrix can be avoided. Experimental results are showed to prove the accuracy and efficiency of the algorithm

In this paper, we propose an algorithm for extracting and selecting SIFT (scale-invariant feature transform) visual features for bearing-only SLAM in indoor environments. The algorithm is based on analyzing the stability of the matching ratio of the SIFT features at different scales, and it is capable of extracting SIFT features that can be matched reliably and, at the same time, lead to accurate landmark initialization. In addition, the algorithm is an order of magnitude more efficient than the original SIFT algorithm and is therefore appropriate for the real-time nature of SLAM. As well, the algorithm can determine the quality of the visual features without any delay, and this eliminates the need for a matching or tracking procedure, as is often necessary in other feature extraction algorithms. Results from several experiments verify the performance of the proposed algorithm