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SUMMARYFastSLAM is a framework for simultaneous localization and mapping using a Rao-Blackwellized particle filter (RBPF). But, FastSLAM is known to degenerate over time due to the loss of particle diversity, mainly caused by the particle depletion problem in resampling phase. In this work, improved particle filter using geometric relation between particles is proposed to restrain particle depletion and to reduce estimation errors and error variances. It uses a KD tree (k-dimensional tree) to derive geometric relation among particles and filters particles with importance weight conditions for resampling. Compared to the original particle filter used in FastSLAM, this technique showed less estimation error with lower error standard deviation in computer simulations.

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This paper addresses the real-time optimization problem of the message-chain structure to maximize the throughput in data communications based on half-duplex command-response protocols. This paper proposes a new variant of the particle swarm optimization (PSO) algorithm to resolve real-time optimization, which is implemented on field programmable gate arrays (FPGA) to be performed faster in parallel and to avoid the delays caused by other tasks on a central processing unit. The proposed method was verified by finding the optimal message-chain structure much faster than the original PSO, as well as reliably with different system and algorithm parameters.