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Seasonal changes of fruit tree crowns and changes of fruit tree characteristics caused by the growth and aging of fruit trees will affect the matching of the three-dimensional environmental map of the orchard. Therefore, an accurate construction algorithm of orchard two-dimensional environmental map was proposed based on improved Gmapping algorithm. In this algorithm, the front-end odometer and the back-end optimization part of Gmapping algorithm were improved respectively, so as to improve the construction accuracy of two-dimensional environment map of orchard. For the front-end odometer part, the improved R-GPF method was used to improve its initial positioning accuracy, and for the back-end optimization part, the BAT heuristic adaptive resampling method was used to improve its final positioning accuracy. Then, the comparative experiment of pear orchard environment was carried out. By comparing the improved R-GPF method with the original R-GPF method, the output frequency of the improved R-GPF LiDAR odometer can reach 15.58Hz, the maximum lateral deviation was less than 25cm, the average lateral deviation was 12.7cm, and the standard deviation was 13.4cm, its performance was superior to that of the original R-GPF LiDAR odometer. Comparing the proposed algorithm with the original Gmapping algorithm based on R-GPF, the distance deviation between pear columns obtained by the proposed algorithm was always within 20cm, and the average distance deviation between rows was 10.3cm, with a standard deviation of 6.3cm, which was 50%, 43.41% and 32.26% lower than that of the original Gmapping algorithm based on R-GPF, respectively. At the same time, the reduction of the distance deviation between pear rows relative to the lateral deviation of odometer reflected the effectiveness of the back-end BAT heuristic adaptive resampling method. The proposed algorithm can improve the accuracy of orchard two-dimensional map construction, and meet the accuracy requirements of subsequent relocation, navigation and other operations.