An Improved Transformed Unscented FastSLAM With Adaptive Genetic Resampling

Fast simultaneous localization and mapping (FastSLAM) is a well-known study for robot navigation. To enhance the performance of FastSLAM, an improved importance sampling is proposed in this paper based on the transformed unscented Kalman filter. The improvement is mainly composed of a novel fuzzy noise estimator, which can adjust the state and observation noises online according to the residual and related covariance, and thus mitigating the defects caused by model inaccuracy. In general, the FastSLAM algorithm suffers from the impoverishment problem since it is essentially a particle filter. Inspired by genetic optimization, an adaptive genetic resampling is proposed to substitute the conventional resampling step to overcome these defects. The proposed method, referred to as the improved transformed unscented FastSLAM, is compared with the unscented FastSLAM and the transformed unscented FastSLAM. The superiorities of the proposed method are verified by simulation and experiment under benchmark environments.