针对单一成像系统获取RGB-D数据时视场过小难以满足大视场成像需求的问题，提出了基于空间信息聚类的RGB-D数据拼接融合方法。根据RGB-D数据中蕴含的空间信息定义物点距离，利用simple linear iterative clustering （SLIC）方法实现空间信息聚类。将场景分割成若干平面子块，每个子块具有单应性，可以进行准确的单应性矩阵计算，进而实现将小视场RGB-D数据准确拼接融合生成大视场RGB-D数据。实验结果表明，所提方法能够改善图像变换过程中产生的扭曲变形及拼接过程中在重叠区域产生的错位。峰值信噪比（PSNR）和结构相似性（SSIM）两个定量评价指标说明基于空间信息聚类的RGB-D数据拼接相较于全局拼接，结果质量得到了提升。

To address the problem of a small field of view of RGB-D data obtained using a single imaging system, where a large field of view is required, an RGB-D data stitching method is proposed based on spatial information clustering. Based on the spatial information present in RGB-D data, the distance between object points is defined to realize spatial information clustering using a simple linear iterative clustering (SLIC) on the RGB-D data. The scene is divided into several planar sub-blocks. Each sub-block shows homography, which can be used to accurately determine the homographic matrix and then realize the accurate splicing and fusion of small-field RGB-D data to generate large-field RGB-D data. Results of a real scene-based experiment shows that the proposed method can decrease the distortion during image warping and reduce the dislocation in overlap regions during stitching. Based on the peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) values, the efficiency of RGB-D data stitching based on spatial information clustering is quantitatively shown to improve compared with global stitching.