Authors

Abstract

Simultaneous localization and mapping (SLAM) is one of the key technologies for agricultural robots to build maps and localize in complex orchard environments and realize unmanned autonomous operations. Due to the complexity of the orchard environment, the single canopy feature and the diffuse reflection of light caused by the leaves, etc., the map construction process of the orchard environment leads to mismatch and increases the cumulative error of the map construction. Aiming at the above problems, this paper propose a navigation map construction method for orchard environment based on the fusion of Scan Context and NDT-ICP. The method firstly searches the Ring key quickly to get the candidate frames, and scores the similarity between the candidate frames and the current frame, and effectively detects the loopbacks by two-stage searching algorithm to reduce the false matches in the map of orchard environment. Meanwhile, a point cloud alignment method based on the fusion of normal distribution transform coarse alignment and iterative nearest point exact alignment is used to reduce the cumulative error of the orchard environment map. The results show that the improved algorithm compensates the drift of the point cloud map with higher mapping accuracy, better real-time performance, lower resource utilization, higher overlap between the trajectory estimation and the real trajectory, smoother loops, and a 4% reduction in CPU occupancy. In the complex orchard environment, the root mean square error and standard deviation of the trajectories of this paper's algorithm are 0.57 m and 0.19 m, which are 68% and 83% higher than those of the loop detection algorithms in the Lightweight Ground Optimized Lidar Trajectory Measurement and Multivariate Terrain Mapping (LeGO-LOAM), respectively. Accurate map construction and low drift pose estimation can be performed.The research algorithm effectively reduces the influence of mis-matching and large cumulative error in the process of map construction in the orchard environment, meets the demand for high-precision environmental mapping in the orchard environment, and provides technical support for promoting unmanned operation in the orchard environment.

Abstract in Chinese

同步定位与绘图（SLAM）是农业机器人在复杂果园环境中构建地图并进行定位、实现无人自主作业的关键技术之一。由于果园环境的复杂性、树冠的单一性和树叶对光线的漫反射等特点，果园环境的地图构建过程中会出现不匹配现象，增加了地图构建的累积误差。针对上述问题，本文提出了一种基于 Scan Context 和 NDT-ICP 融合的果园环境导航地图构建方法。该方法首先快速搜索环键得到候选帧，并对候选帧与当前帧的相似度进行评分，通过两阶段搜索算法有效检测回环，减少果园环境地图中的虚假匹配。同时，采用基于正态分布变换粗配准和迭代最近点精确配准融合的点云配准方法，降低果园环境地图的累积误差。结果表明，改进后的算法可以弥补点云图的漂移，具有更高的映射精度、更好的实时性、更低的资源利用率、更高的轨迹估计与真实轨迹重合度、更平滑的循环以及降低 4% 的 CPU 占用率。在复杂果园环境中，本文算法的轨迹均方根误差和标准偏差分别为0.57米和0.19米，比轻量级地面优化激光雷达轨迹测量和多元地形测绘（LeGO-LOAM）中的环路检测算法分别高68%和83%。该算法有效降低了果园环境地图构建过程中的误匹配和大累积误差的影响，满足了果园环境高精度环境测绘的需求，为推进果园环境无人化作业提供了技术支撑。