This paper proposes a laser-camera fusion Gmapping mapping strategy to resolve problems of incomplete obstacle detection or unsatisfactory mapping effects when carrying lidar or an RGB-D camera on a mobile robot in Gmapping mapping. First, the camera point cloud and laser point cloud are preprocessed, and then point cloud fusion and filtering are performed by the point cloud library (PCL). The point-to-line iterative closest point (PL-ICP) algorithm is used to register the point cloud of adjacent frames to improve the matching accuracy and speed. Second, a visual odometer and a laser odometer are fused by the Kalman filtering algorithm, and the fused data and wheel odometer are dynamically weighted twice to improve the accuracy of odometers. Finally, the proposed method is verified on the built mobile robot. The experimental results show that the proposed method improves the obstacle detection rate by 32.03 percentage points and 19.86 percentage points, respectively, compared to the laser mapping and camera mapping methods, the size error of the map reduces by 0.014 m and 0.141 m, and the angle error decreases by 1° and 3°, respectively. The accuracy of the odometer is increased by 0.12 percentage points compared to the old odometer.