Abstract
To solve the problems of low accuracy and poor real-time performance in traditional mobile robot vision simultaneous positioning and map construction (SLAM), the original algorithm was improved. First, the ORB features of adjacent images are extracted, and the PROSAC algorithm is used to achieve feature point matching. At the same time, the PROSAC algorithm is improved and optimized, and the execution time of the optimized PROSAC algorithm is significantly reduced; finally, based on the graph optimization model, a global Bundle Adjustment algorithm based on the largest common-view weight frame is proposed to achieve dense and sparse map creation. The algorithm is verified by Tum data set, and the experiment shows that the root mean square error has dropped significantly. The results on the data set effectively prove the effectiveness of the algorithm in this paper.
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