Simultaneous Localization And Mapping （SLAM） is a research hotspot in robot localization and navigation. Reliable loop closure detection is critical for graph-based SLAM. However， loop closure detection by vision or Lidar is computationally expensive and has low reliability in large and complex environments. To solve this problem， a graph-based SLAM algorithm based on WiFi fingerprint sequence matching was proposed. In this algorithm， fingerprint sequences were used for loop closure detection. Since the fingerprint sequence contains data of multiple fingerprints， which is considered to be richer than a single fingerprint pair in the amount of information. Therefore， the traditional method based on single fingerprint pair matching was extended to fingerprint sequence matching， which greatly reduced the probability of false loop closure， thus ensuring the high accuracy of loop closure detection and satisfying high precision requirement of SLAM algorithm in large and complex environments. Two sets of experimental data （robots start from different starting points） were used to verify the proposed algorithm. The results show that the proposed algorithm is more accurate than Gaussian similarity method， and has the accuracy on the first and second set of data increased by 22.94% and 39.18% respectively. Experimental results fully verify the superiority of the proposed algorithm in improving the positioning accuracy and ensuring the reliability of loop closure detection

Stereo pair acquisition of a scene is the key to binocular stereo imaging. This paper presented a stereo pair creation method when 3D models were constructed. Using camera objects in 3DS MAX, the method started from a coordinate transformation of objects in the scene based on principle of binocular stereo vision. Then the method carried out the perspective transformation to create left image and right image respectively. The results of the experiment indicate the position of the two target cameras and the 3D model, together with the length of the base line is the key factor that affects the stereo effect. Changing the position of the target cameras and the 3D model may result in positive disparity or negative disparity stereo pairs. When the aspect ratio of AB to CO equals 0.05, the stereo effect of the stereo pairs created is better.