A Novel Self-Positioning Based on Feature Map Creation and Laser Location Method for RBPF-SLAM

Aiming at the high computational complexity of the traditional Rao-Blackwellized Particle Filtering (RBPF) method for simultaneous localization and Mapping (SLAM), an optimization method of RBPF-SLAM system is proposed, which is based on lidar and least square line segment feature extraction as well as raster, reliability mapping continuity. Validation test results show that less storage in constructing a map with this method is occupied, and the computational complexity is significantly reduced. The effect of noise data on feature data extraction results is effectively avoided. It also solves the problem of error accumulation caused by noninteger grid size movement of unmanned vehicle in time update stage based on Markov positioning scheme. The improved RBPF-SLAM method can enable the unmanned vehicle to construct raster map in real time, and the efficiency and accuracy of map construction are significantly improved.

UWB Indoor Localization Algorithm Using Firefly of Multistage Optimization on Particle Filter

With the increasing application potential of indoor personnel positioning, ultra-wideband (UWB) positioning technology has attracted more and more attentions of scholars. In practice, an indoor positioning process often involves multipath and Non-Line-Of-Sight (NLOS) problems, and a particle filtering (PF) algorithm has been widely used in the indoor positioning research field because of its outstanding performance in nonlinear and non-Gaussian estimations. Aiming at mitigating the accuracy decreasing caused by the particle degradation and impoverishment in traditional Sequential Monte Carlo (SMC) positioning, we propose a method to integrate the firefly and particle algorithm for multistage optimization. The proposed algorithm not only enhances the searching ability of particles of initialization but also makes the particles propagate out of the local optimal condition in the sequential estimations. In addition, to prevent particles from falling into the oscillatory situation and find the global optimization faster, a decreasing function is designed to improve the reliability of the particle propagation. Real indoor experiments are carried out, and results demonstrate that the positioning accuracy can be improved up to 36%, and the number of needed particles is significantly reduced.

Application of an improved particle filter for random seismic noise suppression

Random noise is inevitable during seismic prospecting. Seismic signals, which are variable in time and space, are damaged by conventional random noise suppression methods, and this limits the accuracy in seismic data imaging. In this paper, an improved particle filtering strategy based on the firefly algorithm is proposed to suppress seismic noise. To address particle degradation problems during the particle filter resampling process, this method introduces a firefly algorithm that moves the particles distributed at the tail of the probability to the high-likelihood area, thereby improving the particle quality and performance of the algorithm. Finally, this method allows the particles to carry adequate seismic information, thereby enhancing the accuracy of the estimation. Synthetic and field experiments indicate that this method can effectively suppress random seismic noise.