An Omni-directional vSLAM based on spherical camera model and 3D modeling

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.