Stereo matching algorithm for mineral images based on improved BT-Census

Binocular stereo matching is crucial for identifying and locating minerals on the grizzly, allowing the robotic system to carry out crushing autonomously. The traditional stereo matching algorithm yields a low matching rate due to the relatively single color and weak texture of the mineral image caused by the uneven illumination in the field. An improved Birchfield-Tomasi (BT)-Census algorithm is proposed to enhance the capability of discriminating the mineral region and increase the successful matching rate. Firstly, the Gaussian-weighted average grey value of the circular window is used as the central value of the Census transform, and the initial surrogate value is obtained by weighting and fusing the Census cost and the BT cost. Subsequently, the cost aggregation method by adaptive windows is used, and then scanline optimization is applied to select the optimal matching cost. The performance evaluation results using the Middlebury dataset show that the proposed algorithm achieves a 93.33% average successful matching rate, outperforming Absolute Difference of Intensity (AD)-Census, Semi-Global Matching (SGM), and PatchMatch algorithms by 6.5%, 8.04%, and 4.62% respectively. Moreover, In the three-dimensional (3D) reconstruction experiments of minerals on grizzly, the point cloud reconstructed by the proposed method shows significant improvement in terms of accuracy. Notably, in comparison to the SGM algorithm, there is an 83.4% reduction in Mean-Square Error (MSE), a 35.4% reduction in Root Mean-Square Error (RMSE), and a 35.8% reduction in Mean Absolute Error (MAE). Against the AD-Census algorithm, reductions of 47.8% in MSE, 21.6% in RMSE, and 21.4% in MAE are observed. Similarly, in comparison to the PatchMatch algorithm, there are reductions of 11.9% in MSE, 5.8% in RMSE, and 6.1% in MAE. In a word, the proposed improved BT-Census stereo matching algorithm effectively enhances the detailed features of the minerals and improve the successful matching rate and accuracy.