Enhanced resampling scheme for Monte Carlo localization

Abstract

The indoor positioning problem is a critical research domain essential for real-time control of mobile robots. Within this field, Monte Carlo-based solutions have been devised, leveraging the processing of diverse sensor data to address numerous challenges in local and global positioning. This study focuses on resampling strategies within the conventional Monte Carlo framework, which directly impact positioning performance. From this perspective, in contrast to the conventional method of employing weight thresholding and full particle scattering when the robot becomes disoriented, this study proposes an alternative approach. It advocates for a localized space resampling strategy, adaptive noise injection guided by likelihood, and the incorporation of beam rejection modifications to address dynamic (unmapped) obstacles effectively. The real-time experimental results, conducted with varying particle counts, demonstrate that the proposed scheme effectively manages the presence of unmapped obstacles while employing fewer particles than the standard Monte Carlo implementation.