Scan likelihood evaluation in FastSLAM using binary Bayes filter

Abstract

FastSLAM is a fundamental algorithm for Simultaneous Localization and Mapping (SLAM). FastSLAM based on grid map is a popular method to build a map of both the structured and unstructured environment. The performance of FastSLAM significantly depends on evaluation of measurement likelihood. In this paper, we propose a new method to evaluate laser scan likelihood using the binary Bayes filter. This method supports the right particles but does not suffer from particle depletion problem. We implemented the hardware system based on the Pioneer 2-DX platform equipped with the Hokuyo laser scanner. The experimental result shows that the proposed method builds the map accurately.