Mobile robots can be divided into wheeled robots and legged robots. Wheeled robots have strong stability and low complexity, whereas legged robots have strong adaptability to terrain, both of them are the focus in the field of robotics research at present. With the deepening of research on mobile robots, the research focus has gradually shifted from mechanical structure design to improving the robot's adaptability to the external environment, that is, the ability to autonomously perceive and interact with the external environment. The perception of the external environment relies on sensors. Currently, the research on single sensor detection has been relatively mature, such as single visual SLAM and laser SLAM. However, how to fuse the information from the different sensors to improve the accuracy and stability of detection is still a research hotspot. In this paper, a fusion scheme of vision and laser SLAM (Simultaneous Localization and Mapping) is proposed to fuse the information of the above two sensors, and experiments are carried out on wheeled and legged robots respectively. Finally, the experimental results were compared and the possible optimization measures were proposed.
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