复杂果园场景中基于DBP的激光回环检测算法

Loop Closure Detection in Complex Orchards Based on Density Binary Pattern

为减少果园机器人在定位与建图过程中产生的累积漂移误差,本文提出一种基于密度二进制模式(Density binary pattern, DBP)描述子的激光回环检测算法。算法将点云空间分割为二进制单元块,提取包含点云高度与密度信息的全局描述子DBP。针对复杂果园的大尺度、高度相似、非结构化特性,基于两阶段搜索算法实现高效回环检测。基于历史帧DBP的环因子检索K近邻候选帧,确认与当前帧DBP描述子最相似的候选帧为最终目标回环索引。在具有多个回环事件的复杂果园场景中,DBP-LeGO-LOAM算法轨迹的均方根误差与标准差分别为0.24 m与0.09 m,相对LeGO-LOAM中基于距离的回环检测算法分别减少81%与91%。实验证明,本文方法对多回环复杂果园环境具有更好的适应性,为提高果园机器人建图与定位精度提供了有效解决方案。

In order to reduce the cumulative drift error of the orchard robot in simultaneous localization and mapping(SLAM),a loop closure detection algorithm was proposed based on density binary pattern(DBP).The LiDAR scanning was divided into eight-bit binary bins along the vertical height direction.If the number of point clouds in the bin exceeded five,it was considered a valid scan,and the bin value was set to be 1,otherwise 0.Further,the eight-bit data were projected to construct the DBP descriptor.The DBP descriptor contained point cloud density and height information and had a significant distinguishing effect on tall fruit trees and low shrubs.A two-stage search algorithm was utilized to ensure the task real-time requirements in the large-scale orchard.Firstly,to extract a low-dimensional ring factor vector of DBP,the K-nearest neighbor candidate loop closure frames were quickly found in the K-dimensional tree(KD-Tree),which was constructed by the ring factors.The maximum similarity between the candidates and the query frame was obtained.If the distance threshold condition was met,the candidate frame was considered an effective target loop closure.The experiment was carried out in three orchards of different scales.In the orchard scene with multiple loop closure events,the root mean square error and standard deviation of the DBP-LeGO-LOAM trajectory were 0.24 m and 0.09 m,compared with the LeGO-LOAM algorithm which had been reduced 81%and 91%respectively.It provided an effective solution for improving the mapping and localization accuracy of orchard robots.