Laser SLAM (Simultaneous Localization and Mapping) and visual SLAM have been fully developed and widely used in military and civil fields.However,single sensor SLAM has limitations,for instance,laser SLAM is not suitable for scenes with a large number of dynamic objects around it,while visual SLAM has poor robustness in low-texture environments.Therefore,fusion of the two technologies has great potential to compensate each other,and it can be prospected that SLAM technology combining laser and vision or even more sensors will be the mainstream direction in the future.Here,we review the development of SLAM technology,analyze the hardware information of lidar and camera,and introduce some classical open-source algorithms and datasets.Furthermore,the multi-sensor fusion schemes are detailed from perspectives of uncertainty,feature and novel deep learning.The excellent performance of multi-sensor fusion schemes in complex scenes are summarized,and the future development trend of multi-sensor fusion is prospected.