Abstract
We propose a new SLAM method based on fast simultaneous localization and mapping (FastSLAM). The technique presented uses an improved quantum-behaved particles swarm optimization (QPSO) to improve the proposal distribution of particles and optimize the estimated particles. This method makes the sampled particles closer to the true pose of the robot and improves the estimation accuracy of robot poses and landmarks. In the QPSO algorithm, the Gaussian disturbance is added to increase the diversity of the particles. By using this technique the premature convergence of particles swarm is overcome. In the resample step, the threshold value is used to evaluate the particle diversity. When the particle diversity is below the threshold value, the linear optimization is used to produce new sample particles, which increases the particle diversity and eliminates the loss of diversity. Simulations and experiments show that the proposed approach improves the accuracy of SLAM. The accuracy of estimated poses and landmarks with the proposed method is better than that with the traditional SLAM method.
Highlights
Simultaneous and localization and mapping (SLAM) is an active research area in mobile robotics [1, 2]
We propose a new SLAM method based on fast simultaneous localization and mapping (FastSLAM)
In this paper, we propose a new FastSLAM approach improved by Quantum-behaved particles swarm optimization (QPSO-FastSLAM)
Summary
Simultaneous and localization and mapping (SLAM) is an active research area in mobile robotics [1, 2]. The task of SLAM is to build a map while estimating the robot pose relative to the map. Michael Montemerlo proposed FastSLAM to solve the SLAM problem, which had been proved to be an effective method [5, 6]. In FastSLAM, the particle filter is used to estimate the mobile robot poses and the EKF is used to estimate the features. Resample can solve the degeneration problem of particle filters, but it will result in the particle depletion phenomenon. Only highweighted particles survive and are duplicated many times, and the low-weighted particles disappear together with their information about robot poses and features. The diversity of particles decreases; the estimated accuracy of robot poses and features decreases. In particle filters based on SIS (Sequential Importance Sampling), the particle depletion problem is inevitable. Due to the lack of knowledge about the robot poses, it is difficult to maintain the enough effective particles to guarantee the estimated accuracy of robot poses
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