Abstract: To solve the shortcoming of SLAM(simultaneous localization and mapping) that the sparse feature map fails to provide full information about the environment,global observation map model(GOMM) is proposed according to physical meanings of the observations.In GOMM,global dense map information is embedded into sparse feature map,and necessary observations are selected with displacement rule,feature rule,and sensory limit rule.After that,the selected observations are denoised and transformed.Then,global dense map of the environment is built according to physical meanings of the observations and uncertainty of the robotic pose estimation.Monochrome map,gray scale map,or color map of the environment is obtained after visualization.By combining GOMM with EKF-SLAM(extended Kalman filter SLAM),an algorithm named GOE-SLAM(global observation EKF-SLAM) is put forward.Experiments with "Car Park Dataset" are carried out to evaluate the performance of GOE-SLAM.Experimental results indicate that a reliable dense map is built with GOE-SLAM, and the computational complexity of GOE-SLAM is nearly equal to that of EKF-SLAM.