Low-cost, commercial RGB-D cameras have become one of the main sensors for indoor scene 3D perception and robot navigation and localization. In these studies, the Intel RealSense R200 sensor (R200) is popular among many researchers, but its integrated commercial stereo matching algorithm has a small detection range, short measurement distance and low depth map resolution, which severely restrict its usage scenarios and service life. For these problems, on the basis of the existing research, a novel infrared stereo matching algorithm that combines the idea of the semi-global method and sliding window is proposed in this paper. First, the R200 is calibrated. Then, through Gaussian filtering, the mutual information and correlation between the left and right stereo infrared images are enhanced. According to mutual information, the dynamic threshold selection in matching is realized, so the adaptability to different scenes is improved. Meanwhile, the robustness of the algorithm is improved by the Sobel operators in the cost calculation of the energy function. In addition, the accuracy and quality of disparity values are improved through a uniqueness test and sub-pixel interpolation. Finally, the BundleFusion algorithm is used to reconstruct indoor 3D surface models in different scenarios, which proved the effectiveness and superiority of the stereo matching algorithm proposed in this paper.

中文翻译：

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RGB-D摄像机室内3D感知的实时红外立体匹配算法

低成本的商用RGB-D摄像机已成为室内场景3D感知以及机器人导航和定位的主要传感器之一。在这些研究中，英特尔实感R200传感器（R200）在许多研究人员中都很流行，但是其集成的商业立体声匹配算法检测范围小，测量距离短和深度图分辨率低，严重限制了其使用场景和使用寿命。针对这些问题，在现有研究的基础上，提出了一种结合半全局方法和滑动窗口思想的红外立体匹配算法。首先，对R200进行校准。然后，通过高斯滤波，左右立体声红外图像之间的相互信息和相关性得到增强。根据共同的信息，实现了匹配中的动态阈值选择，提高了对不同场景的适应性。同时，Sobel算子在能量函数的成本计算中提高了算法的鲁棒性。另外，通过唯一性测试和子像素插值来提高视差值的准确性和质量。最后，将BundleFusion算法用于在不同场景下重建室内3D表面模型，证明了本文提出的立体匹配算法的有效性和优越性。通过唯一性测试和亚像素插值，可以提高视差值的准确性和质量。最后，将BundleFusion算法用于在不同场景下重建室内3D表面模型，证明了本文提出的立体匹配算法的有效性和优越性。通过唯一性测试和亚像素插值，可以提高视差值的准确性和质量。最后，将BundleFusion算法用于在不同场景下重建室内3D表面模型，证明了本文提出的立体匹配算法的有效性和优越性。