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With the aim of meeting the autonomous navigation requirements of small and medium-sized agricultural machinery in tunnel greenhouse with no crops or low crops, a navigation line fitting method-based on steel tube detection was proposed. Firstly, the indoor environment structure was analyzed. Secondly, the greenhouse point cloud data was collected by LiDAR, and the nearest side window point convergence was extracted from the 3D laser point cloud data by Euclidian transformation, pass-through filtering and DBSCAN clustering. Then a steel tube detection method was proposed with the intention of obtaining stable steel tube point cloud, which iterated through all the scanning lines of the target window point cloud, considering its spatial location and other factors, set different thresholds to filter out the plastic film point cloud and extract the qualified steel tube point cloud. Finally, a straight line fitted by the steel tube point cloud was obtained by principal component analysis, and the navigation parameters of the platform in the greenhouse were determined by using this as the navigation reference line. The steel tube detection test showed that the efficiency of the steel tube detection method in tunnel greenhouses was not less than 88%, and the navigation reference line fitted by the proposed algorithm had a high updating frequency. In the navigation parameter measurement accuracy test, the mean absolute errors of cross range and yaw angle were 0.03 m and 2.12°, respectively. The research result can meet the requirements of autonomous navigation of agricultural machinery in a tunnel greenhouse, which can provide reference for the research of autonomous navigation in this environment.