Particle filtering for WSN aided SLAM

the paper proposed a particle filter based and Wireless Sensor Networks (WSN) aided Simultaneous Localization and Mapping (SLAM) strategy. The proposed method aims at solving two troublesome problems in the traditional particle filter based SLAM algorithms. The first problem is high dimension of question space; and the second one is multi-date association. Firstly, the paper analysed the model of the WSN aided SLAM problem. Then noises in the model were analysed. According to analyses, a particle filtering algorithm was developed as the kernel algorithm for data fusion. Detailed procedures of the particle filter were introduced. Besides, all key steps, including initialization, prediction, sequential importance sampling and also resampling, were especially specified. Software simulations were done to analyse and prove the validity and the efficiency of the proposed method. The simulation results supported that the proposed method can diminish dimensions of the SLAM problem and resolve the multi-data association problems. Moreover, through adopting PF algorithm and involving WSN, following advantages were also acquired: firstly, the method could locate blind nodes in WSN with high resolution while no anchor node was available; secondly, it could improve precision of localization and mapping for mobile robots and, especially, it could inhibit error accumulation of dead reckoning without requirement of close-loop.