Robot Fusion Localization Method Based on Adaptive Sequential Filtering
Aiming at the low robustness of lidar in long straight environment and the problem that the visual camera is greatly af-fected by the illumination conditions,this paper proposes a positioning method using sequential Kalman filter to fuse the informa-tion collected by the two sensors,which transfers the sensors as independent nodes step by step to realize multi-level filtering.At the same time,the adaptive component is added to the algorithm to avoid the divergence phenomenon caused by the lack of dynamic adjustment in the long-distance driving process of mobile robot.Simulation results show that in the form of adaptive sequential fil-tering,the fusion positioning results significantly reduce the error caused by single sensor measurement,and effectively improve the positioning accuracy of mobile robot in unknown environment.
Sequential FilteringMulti Sensor FusionRoboticsLidarVisual Camera
NETL
NSTL
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