Recovering an outdoor environment's surface mesh is vital for an agricultural robot during task planning and remote visualization. Our proposed solution is based on a newly-designed panoramic stereo camera along with a hybrid novel software framework that consists of three fusion modules. The panoramic stereo camera with a pentagon shape consists of 5 stereo vision camera pairs to stream synchronized panoramic stereo images for the following three fusion modules. In the disparity fusion module, rectified stereo images produce the initial disparity maps using multiple stereo vision algorithms. Then, these initial disparity maps, along with the intensity images, are input into a disparity fusion network to produce refined disparity maps. Next, the refined disparity maps are converted into full-view point clouds or single-view point clouds for the pose fusion module. The pose fusion module adopts a two-stage global-coarse-to-local-fine strategy. In the first stage, each pair of full-view point clouds is registered by a global point cloud matching algorithm to estimate the transformation for a global pose graph's edge, which effectively implements loop closure. In the second stage, a local point cloud matching algorithm is used to match single-view point clouds in different nodes. Next, we locally refine the poses of all corresponding edges in the global pose graph using three proposed rules, thus constructing a refined pose graph. The refined pose graph is optimized to produce a global pose trajectory for volumetric fusion. In the volumetric fusion module, the global poses of all the nodes are used to integrate the single-view point clouds into the volume to produce the mesh of the whole garden. The proposed framework and its three fusion modules are tested on a real outdoor garden dataset to show the superiority of the performance.

翻译：恢复室外环境表面网格对于农业机器人在任务规划和远程可视化过程中至关重要。我们提出的解决方案基于新设计的全景立体相机及由三个融合模块构成的混合新型软件框架。该五边形全景立体相机包含5组立体视觉相机对，用于为后续三个融合模块流式传输同步全景立体图像。在视差融合模块中，经校正的立体图像通过多种立体视觉算法生成初始视差图，随后将这些初始视差图与强度图像输入视差融合网络以生成精炼视差图。接着，将精炼视差图转换为全景点云或单视角点云，用于位姿融合模块。该模块采用两阶段全局粗配准到局部精配准策略：第一阶段通过全局点云匹配算法对每对全景点云进行配准，以估计全局位姿图边的变换，有效实现闭环检测；第二阶段利用局部点云匹配算法匹配不同节点中的单视角点云。随后依据三条规则局部优化全局位姿图中所有对应边的位姿，从而构建精炼位姿图。该精炼位姿图经优化后生成全局位姿轨迹，用于体素融合。在体素融合模块中，利用所有节点的全局位姿将单视角点云整合至体素中，生成整个园艺场的网格。所提框架及其三个融合模块在真实室外园艺数据集上进行了测试，验证了性能的优越性。