Improvements based on Adaptive Monte Carlo Localization

This paper presents an improved indoor localization and navigation method primarily based on Channel State Information (CSI) fingerprinting and Adaptive Monte Carlo Localization (AMCL) technology. Wi-Fi localization offers convenience and flexibility, allowing it to be applied in different environments and easily extended to cover larger areas. However, it lacks accuracy. On the other hand, the AMCL algorithm provides precision but is limited by indoor environmental constraints and complex computations. By combining the two, this method addresses these issues without requiring excessive computational complexity, resulting in a more accurate and efficient localization system. Experimental results show that this approach significantly enhances the global localization accuracy and navigation capabilities of robots in complex indoor environments. This achievement not only optimizes the performance of indoor robot navigation systems but also provides an effective implementation strategy for future indoor localization technologies.