To address the problems of visual feature loss, radar closed-loop trajectory vector drift, and elevation pose deviation in vision and laser coupled simultaneous localization and mapping (SLAM), a close coupled vision and lidar SLAM method based on scanning context loop detection is proposed. A visual odometer based on SIFT and the ORB feature point detector is used to solve the problem of feature point loss and matching failure. A radar odometer eliminates the distortion and large drift of the radar point cloud by fusing the inter-frame estimation of the visual odometer. Loopback detection is performed by scanning context, and the vector drift of the odometer is optimized by introducing the factor graph to eliminate loopback detection failure. The proposed algorithm is verified on several KITTI datasets and compared with classical algorithms. The experimental results show that the algorithm exhibits high stability, strong robustness, low drift, and high accuracy.