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Urban laser radar point cloud building extraction is a hot spot in recent years, but the accurate distinction between vegetation, buildings and man-made objects has always been a difficult point. In this paper, a point cloud classification algorithm based on ICSF and weakly correlated random forest are proposed for the problem of low classification accuracy. Firstly, the data is ground-filtered by ICSF algorithm, then the decision tree is constructed, and correlation analysis is performed based on the maximum mutual information coefficient. A decision tree with the smallest correlation coefficient and the highest precision is selected to form a random forest. Finally, the decision results are weighted and completed. Point cloud classification. This paper validates the model through the Vaihingen city dataset and ranks the importance of the features according to the method of reducing the average precision. Compared with the traditional random forest classification algorithm, the classification accuracy is improved by 4.2%, which shortens the model time.

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In order to improve the positioning accuracy and reduce the localization cost, a kind of PSO-based RFID indoor localization algorithm is proposed in this paper. The main idea of this algorithm contains the following two aspects. First, due to the influence of none line of sight and multipath transmission in indoor environment, we adopt Gaussian Smoothing Filter to process Received Signal Strength Indicator (RSSI) values, which can reduce the impact of environmental factors on the position estimation effectively. Second, Particle of Swarm Optimization (PSO) algorithm is introduced to obtain a better positioning result. By experimenting in different indoor environment, the results demonstrate that the proposed approach can not only improve the precision of indoor localization, but has a lower cost and better robustness when compared to VIRE approach.

For the realtime updating and strong randomness of the information, large RFID-based warehousing picking path optimization job requires to make decision continuously. It is different from the traditional picking path Problem. This paper proposed an Improved Ant Colony algorithm in order to solve the optimization of the large storage picking path based on RFID. Distributed Strategy, Dynamic Response Strategy and Time Waiting Strategy are adopted to improve the candidate set, meanwhile, adjust operator and parameter selection. Experimental results show that the convergence speed of this algorithm with high precision, a better solution to the optimization of picking operation based on RFID.