The invention discloses a point-line feature combined binocular vision SLAM method. The method comprises the following steps: S1, calibrating internal parameters of a binocular camera; S2, using the calibrated camera to collect an environment image, and using a gradient density filter to filter a feature dense region to obtain an effective image detection region; S3, extracting feature points andfeature lines; S4, performing broken line combination on the extracted line features; S5, performing tracking matching by utilizing the feature point line, and selecting a key frame; S6, constructinga cost function by using the re-projection error of the feature point line; S7, performing local map optimization; And S8, judging a closed loop by using the point-line combined word bag model, and optimizing the global track. The invention provides an image filtering mechanism, a line segment merging method and an accelerated back-end optimization calculation method, solves the problems that a large number of invalid features are extracted from an image feature dense region, a line feature extraction method is broken, a traditional back-end optimization method is long in time consumption andthe like, and improves the robustness, the precision and the speed of a system.