Monte Carlo Localization (MCL) algorithm has many problems such as large computation and poor positioning accuracy. Because of the diversity of information carried by two-dimensional code and usability and convenience of two-dimensional code recognition, an adaptive MCL algorithm integrated with two-dimensional code information was proposed. Firstly, the cumulative error of odometer model was corrected by absolute position information provided by two-dimensional code and then sampling was performed. Sencondly, the measurement model provided by laser sensor was used to determine the importance weights of the particles. Finally, as fixed sample set used in the resampling part caused large computation, Kullback-Leibler Distance (KLD) was utilized in resampling to reduce the computation by adaptively adjusting the number of particles required for the next iteration according to the distribution of particles in state space. Experimental result on the mobile robot show that the proposed algorithm improves the localization accuracy by 15.09% and reduces the localization time by 15.28% compared to traditional Monte-Carlo algorithm.

The vectorization of documents affects the speed and accuracy of text categorization greatly.The most common used formulas: TF-IDF,MI,and IG were analyzed.The method of feature selection based on word frequency differentia was proposed and TF-IDF formula was modified to improve the quality of feature selection,the speed and accuracy of categorization.