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基于改进Gmapping算法的果园二维环境地图精准构建

Accurate Construction of Orchard Two-dimensional Environmental Map Based on Improved Gmapping Algorithm

  • 摘要: 果树树冠的季节性变化及果树成长和衰老造成的果树特征变化会影响已构建的果园三维环境地图的匹配,故本文提出一种基于改进Gmapping算法的果园二维环境地图精准构建算法。首先该算法对Gmapping算法的前端里程计和后端优化部分分别进行改进,以提高果园二维环境地图的构建精度。对于前端里程计部分采用改进的R-GPF地面分割方法提高其初始定位精度,对于后端优化部分采用BAT启发式自适应重采样方法提高其最终定位精度。然后进行梨园环境对比试验。通过改进R-GPF方法与原始R-GPF方法的对比,改进R-GPF方法的激光雷达里程计输出频率可达到15.58 Hz,最大横向偏差小于25 cm,横向偏差均值为12.7 cm,标准差为13.4 cm,其各方面性能都优于原R-GPF方法的激光雷达里程计。通过新算法与基于原R-GPF的Gmapping算法对比,新算法所得的梨树列间距离偏差始终保持在20 cm范围内,行间距离偏差均值为10.3 cm,标准差为6.3 cm,比基于原R-GPF的Gmapping算法分别减小50%、43.41%和32.26%;同时,梨树行间距离偏差相对于里程计横向偏差的减小侧面反映出后端BAT启发式自适应重采样方法的有效性。本文提出的算法能够提高果园二维地图构建精度,可以满足后续重定位、导航等作业的精度要求。

     

    Abstract: 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 15.58 Hz, the maximum lateral deviation was less than 25 cm, the average lateral deviation was 12.7 cm, and the standard deviation was 13.4 cm, 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 20 cm, and the average distance deviation between rows was 10.3 cm, with a standard deviation of 6.3 cm, 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.

     

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