Abstract
Fast simultaneous localization and mapping (FastSLAM) is an efficient algorithm for autonomous navigation of mobile vehicle. However, FastSLAM must reconfigure the entire vehicle state equation when the feature points change, which causes an exponential growth in quantities of computation and difficulties in isolating potential faults. In order to overcome these limitations, an improved FastSLAM, based on the distributed structure, is developed in this paper. There are two state estimation parts designed in this improved FastSLAM. Firstly, a distributed unscented particle filter is used to avoid reconfiguring the entire system equation in the vehicle state estimation part. Secondly, in the landmarks estimation part, the observation model is designed as a linear one to update the landmarks states by using the linear observation errors. Then, the convergence of the proposed and improved FastSLAM algorithm is given in the sense of mean square. Finally, the simulation results show that the proposed distributed algorithm could reduce the computational complexity with high accuracy and high fault-tolerance performance.
Highlights
Simultaneous localization and mapping (SLAM) is the process of enabling a mobile robot to move through an unknown environment, building a map and estimating the position simultaneously, by estimating the features of the environment
We compared the estimated results of the normal FastSLAM algorithm, the distribute SLAM (DPF-SLAM) algorithm, and the distributed FastSLAM algorithm proposed in this paper
We know that the performance of the improved distributed FastSLAM system is better than the others. These results demonstrate that the improved distributed FastSLAM algorithm is workable and stable and has a higher accuracy at the same time
Summary
Simultaneous localization and mapping (SLAM) is the process of enabling a mobile robot to move through an unknown environment, building a map and estimating the position simultaneously, by estimating the features of the environment. The computational complexity of PF-SLAM is a major problem for estimating the robot state and landmarks together To overcome the former problems, a FastSLAM framework, using the RaoBlackwellised particle filter (RBPF), is proposed in [5]. FastSLAM is an efficient algorithm for SLAM problems It decomposes the entire SLAM system into a robot localization problem using PF and a collection of landmarks estimation problems using EKF. The FastSLAM approach separates the robot state from the landmarks state estimate; only the robot state equation needs to be reconfigured when the feature points change in the process. In the improved FastSLAM algorithm, the SLAM problem is divided into robot localization problem and landmarks estimation problem [6].
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