Authors

Abstract

In agricultural operation scenarios, the diversity of farmland terrain, crops and other forms, as well as uncertain factors such as weather changes and crop growth during agricultural operation, can have an impact on the construction of high-precision maps. In order to address these challenges and analyze operational scenarios based on the characteristics of agricultural scenarios, this paper proposes a point cloud map construction algorithm for plant point removal and locatability esti-mation. Based on the existing SLAM framework, plant point removal and locatability estimation are improved. Firstly, RGB images and NIR images are fused to identify and remove plant point clouds, preserving effective inter frame matching information, reducing the impact of dynamic points on inter frame matching, and achieving high front-end motion estimation accuracy. Then, the locali-zation estimation method based on learning is used to determine the motion estimation status and determine whether to execute the backend optimization algorithm. Finally, the back end optimi-zation algorithm based on Factor graph is designed, and the Factor graph, constraint relationship and optimization function are constructed to optimize the pose of all frames. The optimized map construction algorithm reduces the re projection errors between field roads, paths, and crop rows by 10.27%, 20.76%, and 14.36% compared to before optimization. To verify the actual operational effectiveness of the point cloud map construction algorithm, the hardware part of the multi-sensor information collection system was designed, and sensor internal and external parameter calibration were also carried out. A map information collection vehicle was built and field experiments were conducted. The results showed that the positioning error of the point cloud map construction method proposed in this paper is less than 0.5 °, and the cumulative error of 30m translation is less than 12cm, which meets the actual operational requirements.

Abstract in Chinese

在农业作业场景中，农田地形、作物等形态的多样性，以及农业作业过程中的天气变化、作物生长等不确定因素，都会对高精度地图的构建产生影响。为了应对这些挑战，并根据农业场景的特点分析操作场景，本文提出了一种用于植物点去除和定位估计的点云地图构建算法。在现有SLAM框架的基础上，对植物点去除和可定位性估计进行了改进。首先，将RGB图像和近红外图像融合，识别和去除植物点云，保留了有效的帧间匹配信息，减少了动态点对帧间匹配的影响，实现了较高的前端运动估计精度。然后，使用基于学习的局部估计方法来确定运动估计状态，并确定是否执行后端优化算法。最后，设计了基于因子图的后端优化算法，构造了因子图、约束关系和优化函数，对所有帧的姿态进行优化。与优化前相比，优化后的地图构建算法将田间道路、路径和作物行之间的重新投影误差分别降低了10.27%、20.76%和14.36%。为了验证点云地图构建算法的实际操作有效性，设计了多传感器信息采集系统的硬件部分，并进行了传感器内外参数校准。建造了地图信息采集车，并进行了野外试验。结果表明，本文提出的点云地图构建方法定位误差小于0.5°，30m平移累积误差小于12cm，满足实际操作要求。