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本文针对未知环境的机器人自主定位与地图绘制，利用全向图像采集设备，获得球面全景图像，以球形摄像机模型的视图理论和激光建模方法为基础，对周围环境进行三维建模与机器人的自主定位，同时进行地图的绘制与更新。采用球面的SURF特征提取与匹配方法，获得稳键的特征点，据此对机器人位姿进行高精度估计。在系统的实时更新方面，将Particle滤波和Kalman滤波结合起来，用Particle 滤波做整个路径的位姿估计，Kalman滤波用来估计环境特征位置。实验结果表明，本文提出的算法不但具有视角宽广，对位置环境适应性强的优点，而且在机器人对未知环境的全景漫游方面表现良好，具有很高的实时性和鲁棒性。“,”This paper presents an efficient Omni-directional Visual Simultaneous Localization and Mapping (vSLAM) algorithm based on spherical camera model and 3D modeling. In the paper, the robot has the ability of Omni-directional vision, which makes the algorithm more adaptive in an unknown environment. To get spherical panoramic images, we choose the panoramic image acquisition and mosaic equipment (divergent camera cluster). The improved SURF on spherical image, is adopted for feature extraction and matching. According to the theory of multiple view geometry of the spherical camera model, the 3D modeling is conducted for the surrounding environment. By using the feature points with high robustness, the location and pose of the robot can be estimated. In the process of system updating, the particle filter combined with Kalman filter is used for it can perform well in a complex environment. The results of numerical simulations and experiments have been included in this paper to verify the performance of the proposed approach.