Global matching of point clouds for scan registration and loop detection

We present a robust Global Matching technique focused on 3D mapping applications using laser range-finders. Our approach works under the assumption that places can be recognized by analyzing the projection of the observed points along the gravity direction. Relative poses between pairs of 3D point clouds are estimated by aligning their 2D projective representations and benefiting from the corresponding dimensional reduction. We present the complete processing pipeline for two different applications that use the global matcher as a core component: First, the global matcher is used for the registration of static scan sets where no a-priori information of the relative poses is available. It is combined with an effective procedure for validating the matches that exploits the implicit empty space information associated to single acquisitions. In the second use case, the global matcher is used for the loop detection required for 3D SLAM applications. We use an Extended Kalman Filter to obtain a belief of the map poses, which allows to validate matches and to execute hierarchical overlap tests, which reduce the number of potential matches to be evaluated. Additionally, the global matcher is combined with a fast local technique. In both use cases, the global reconstruction problem is modeled as a sparse graph, where scan poses (nodes) are connected through matches (edges). The graph structure allows formulating a sparse global optimization problem that optimizes scan poses, considering simultaneously all accepted matches. Our approach is being used in production systems and has been successfully evaluated on several real and publicly available datasets.本文提出一种面向激光测距仪三维建图应用的鲁棒全局匹配技术。该方法基于如下假设：可通过分析观测点沿重力方向的投影实现场景识别。通过对齐三维点云对的二维投影表示，并结合对应的降维操作，可估算出点云对之间的相对位姿。 本文还给出了以该全局匹配器为核心组件的两类不同应用的完整处理流程：其一，将全局匹配器用于无相对位姿先验信息的静态扫描帧集配准，并结合一种有效的匹配验证流程，该流程可利用与单次采集相关的隐式空空间信息来校验匹配结果。其二，将全局匹配器用于三维同步定位与建图（3D SLAM）应用所需的回环检测任务。本文采用扩展卡尔曼滤波（Extended Kalman Filter）获取地图位姿的置信度，以此实现匹配验证并执行层级重叠度测试，从而减少待评估的潜在匹配数量。此外，该全局匹配器还可与快速局部匹配技术相结合。 在两种应用场景中，全局重建问题均被建模为稀疏图：其中扫描位姿作为节点，匹配关系作为边以连接各节点。该图结构可构建稀疏全局优化问题，在综合考虑所有已接受匹配的同时优化扫描位姿。 目前该方法已应用于生产系统，并在多个真实公开数据集上完成了成功验证。