Abstract: To improve the environmental perception ability of lawn mowing robot during autonomous operation, the obstacle detection method based on the fusion of cameras and low-cost solid-state lidar was proposed. Based on the improved DBSCAN clustering algorithm, the KANN-DBSCAN algorithm was proposed to adaptively determine the clustering parameters. By the algorithm, the 3D point cloud collected by the solid-state lidar was clustered and analyzed, and the obstacle point cloud was obtained and passed through the camera. The results of joint calibration with solid-state lidar were projected onto 2D. The obstacle sample training was completed based on the single short multibox detector（SSD） target detection network, and the image information was detected and recognized to complete the camera-based obstacle detection. To avoid the limited visual or radar detection performance due to the insufficient light or the difficulty of sparse clustering of long-distance radar point clouds, the target-level information fusion strategy with complementary advantages was proposed. The experimental results show that based on the fusion of the detection results of the two sensors, the proposed information fusion strategy can be used under the change of environmental conditions. When the detection performance of single sensor is limited, the missed detection and false detection of environmental perception can be effectively avoided, and the comprehensive detection rate of obstacles after information fusion is about 87.5%, which is significantly improved compared to single sensor and makes the environmental perception information more comprehensive and reliable.