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.

中文翻译：

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基于改进BT-Census的矿物图像立体匹配算法


双目立体匹配对于识别和定位灰熊上的矿物至关重要，从而使机器人系统能够自主进行破碎。由于现场光照不均匀导致矿物图像颜色相对单一、纹理较弱，传统的立体匹配算法匹配率较低。提出一种改进的Birchfield-Tomasi (BT)-Census算法，增强矿区判别能力，提高匹配成功率。首先，将圆形窗口的高斯加权平均灰度值作为Census变换的中心值，通过对Census成本和BT成本进行加权融合得到初始代理值。随后，使用自适应窗口的成本聚合方法，然后应用扫描线优化来选择最佳匹配成本。使用Middlebury数据集进行的性能评估结果表明，该算法平均匹配成功率为93.33%，分别比绝对强度差（AD）-Census、半全局匹配（SGM）和PatchMatch算法分别高出6.5%、8.04% 、 和 4.62% 。此外，在灰熊上矿物的三维（3D）重建实验中，该方法重建的点云在精度方面表现出显着的提高。值得注意的是，与 SGM 算法相比，均方误差 (MSE) 降低了 83.4%，均方根误差 (RMSE) 降低了 35.4%，平均绝对误差 (MAE) 降低了 35.8% 。与 AD-Census 算法相比，MSE 降低了 47.8%，RMSE 降低了 21.6%，MAE 降低了 21.4%。同样，与 PatchMatch 算法相比，MSE 降低了 11.9%，5。RMSE 为 8%，MAE 为 6.1%。总之，所提出的改进BT-Census立体匹配算法有效增强了矿物的细节特征，提高了匹配成功率和准确率。