To improve the efficiency of the semi-global matching (SGM) algorithm, a stereo matching algorithm based on better matching cost calculation and a path optimization strategy is proposed. In the cost calculation stage, the local binary patterns (LBP) operator was optimized by taking diagonal points to reduce time complexity and memory usage; in the cost aggregation stage, five directions were selected for scan line optimization according to the aggregation logic, combined with gray-scale similarity and distance constraint conditions to perform adaptive weight assignment; then the initial disparity value was calculated using the winner-takes-all (WTA) strategy, and the aggregation path was further optimized by the disparity map through left and right consistency detection and the quadratic polynomial interpolation algorithm. Finally, the algorithm’s matching efficiency was validated using the Middlebury 2.0 and 3.0 data platforms. The experimental results show that when compared with the SGM algorithm, the proposed algorithm reduces the time used in the cost calculation stage by 63.1% and that in the cost aggregation stage by 39.3%. When the matching accuracy is slightly improved, the overall efficiency of the algorithm is increased by 54.2%, achieving the goal of efficiency improvement.