基于筛选提取和局部搜索的视觉SLAM优化算法OA

Point-line feature fusion enables feature-based visual Simultaneous Localization and Mapping(SLAM)to achieve continuous and accurate tracking across scenes with varying textures,but it suffers from limited real-time performance.To im-prove computational efficiency,we investigate the correlations between line length,line density,line aggregation degree,and line feature accuracy.Theoretical analysis and experiments demonstrate that line aggregation degree is strongly correlated with line feature accuracy.On this basis,we propose a line feature screening method based on line aggregation degree,which limits the maximum number of line features extracted and only retains line features with high accuracy.Additionally,global brute-force matching is replaced with local search matching,thereby narrowing the search space for candidate line features and reducing the number of descriptor distance calculations.Experimental results show that when the maximum number of line features is set to 150,the algorithm achieves higher accuracy and better real-time performance,yielding the best overall performance.After screen-ing based on line aggregation degree,real-time performance of the algorithm is improved by up to 32%without sacrificing accu-racy.After adopting local search matching,real-time performance is improved by up to about 35%without sacrificing accuracy.