Abstract:

In response to the limited robustness caused by the narrow field-of-view of traditional cameras and the constraints imposed by image distortion in existing panoramic Simultaneous Localization and Mapping (SLAM) algorithms, this study proposes a novel panoramic visual SLAM technology based on spherical mapping. By constructing a spherical grid using Goldberg polyhedra, the mapping relationship between pixels in panoramic two-dimensional images and spherical pixels is established, enabling feature extraction and matching on the spherical grid. Pose estimation is constrained by the polar curve equation, which facilitates the determination of three-dimensional point coordinates. Moreover, the Jacobian matrix of the optimization variables is derived to realize panoramic SLAM. This method fully exploits the geometric characteristics of panoramic cameras, effectively extracts information from panoramic images, and mitigates the effects of distortion. Experimental results demonstrate that the proposed algorithm enhances the capability of extracting information from panoramic image features, increases the quantity and accuracy of feature matches, and ensures the SLAM algorithm′s trajectory accuracy. Compared to the existing OpenvSLAM algorithm, the proposed method achieves higher localization precision and stability.

Key words: panoramic vision, Simultaneous Localization and Mapping (SLAM), spherical mapping, feature extraction, pose solution

摘要：

针对传统相机视场角狭窄所导致的鲁棒性差，而现有全景同步定位与地图构建(SLAM)算法受限于图像畸变的问题，提出一种基于球面映射的全景视觉SLAM技术。通过戈德堡多面体构建球面网格，建立全景二维图像像素与球面像素的映射关系，实现在球面网格上进行特征提取与匹配。引入极弧线方程约束位姿求解，进而求解三维点坐标，并推导优化变量的雅可比矩阵，实现全景SLAM。该方法充分利用全景相机的几何特性并充分提取全景图像的信息，减小了畸变产生的影响。实验结果表明，该方法可以提升全景图像特征的信息提取能力，提高特征匹配的数量和准确率，同时保证SLAM算法的轨迹精度，相较于现有的OpenvSLAM算法，具有更高的定位精度和稳定性。

关键词: 全景视觉, 同步定位与地图构建, 球面映射, 特征提取, 位姿解算