Dual FastSLAM: Dual Factorization of the Particle Filter Based Solution of the Simultaneous Localization and Mapping Problem

Particle Filter-Based SLAM from Localization Viewpoint

In order to enhance consistency in simultaneous localization and mapping (SLAM), in this paper, this problem is considered as a solely localization problem in the presence of unknown parameters. In this approach, the proposal distribution is generated based on marginal extended particle filter and static map is considered as a parametric estimation that is estimated by maximum likelihood techniques. Significant improvement of the filtering result from this viewpoint is demonstrated in terms of estimation performance and consistency. Some simulations and experiments are presented to evaluate the algorithm’s performance in comparison to conventional methods.

A NOVEL PARTICLE FILTER BASED SLAM

In this paper, a new approach to SLAM is proposed that is based on particle filter and soft computing techniques. In this approach, the robot pose is estimated based on unscented marginal particle filter (UMPF) and the static map is considered as parameters that are updated using soft computing. Significant improvement in the proposed method is observed in terms of accuracy of estimation and consistency compared to conventional methods. A number of simulations and experiments are presented to evaluate the algorithm's performance compared to conventional approaches.