This paper introduces a positioning method that utilizes volume kalman Filtering to integrate data from Lidar and visual camera sensors to address the challenges of Lidar robustness in long,straight environments and the impact of illumina- tion conditions on visual camera accuracy.At the same time,an adaptive component is added to the algorithm to improve the positioning accuracy of mobile robots in unknown environments.Firstly,the Lidar and vision are used to observe the sur- rounding objects at the same position simultaneously,and the current robot position information is obtained by using the graph optimization algorithm and pnp algorithm,and then the data collected by the Lidar and vision are continuously updated as the state and measurement values respectively to obtain the filtered fused localization results.The adaptive correction term is added through sage-Husa adaptive filtering theory to solve the problem of data divergence after long-distance observation.The simu- lation results show that the fusion positioning error is reduced by more than 25% compared with laser and vision in the form of an adaptive volume kalman Filter,which effectively improves the positioning accuracy of the mobile robot in the long-distance driving process.