Point cloud registration is a fundamental and challenging problem for autonomous robots interacting in unstructured environments for applications such as object pose estimation, simultaneous localization and mapping, robot-sensor calibration, and so on. In global correspondence-based point cloud registration, data association is a highly brittle task and commonly produces high amounts of outliers. Failure to reject outliers can lead to errors propagating to downstream perception tasks. Maximum Consensus (MC) is a widely used technique for robust estimation, which is however known to be NP-hard. Exact methods struggle to scale to realistic problem instances, whereas high outlier rates are challenging for approximate methods. To this end, we propose Graph-based Maximum Consensus Registration (GMCR), which is highly robust to outliers and scales to realistic problem instances. We propose novel consensus functions to map the decoupled MC-objective to the graph domain, wherein we find a tight approximation to the maximum consensus set as the maximum clique. The final pose estimate is given in closed-form. We extensively evaluated our proposed GMCR on a synthetic registration benchmark, robotic object localization task, and additionally on a scan matching benchmark. Our proposed method shows high accuracy and time efficiency compared to other state-of-the-art MC methods and compares favorably to other robust registration methods.

翻译：点云配准是自主机器人在非结构化环境中进行物体位姿估计、同步定位与建图、机器人-传感器校准等应用时面临的基础性且具有挑战性的问题。在基于全局对应关系的点云配准中，数据关联是一项高度脆弱的任务，通常会产生大量外点。未能有效剔除外点可能导致误差传播至下游感知任务。最大一致性（MC）是一种广泛使用的鲁棒估计技术，但已知其为NP难问题。精确方法难以扩展至实际规模的问题实例，而高外点率对近似方法构成挑战。为此，我们提出基于图的最大一致性配准（GMCR），该方法对外点具有高度鲁棒性，并可扩展至实际规模的问题实例。我们提出新颖的一致性函数，将解耦的MC目标映射至图域，在其中通过最大团找到最大一致性集的紧近似。最终位姿估计以闭式解给出。我们在合成配准基准、机器人目标定位任务以及扫描匹配基准上对提出的GMCR进行了广泛评估。与其他前沿MC方法相比，我们提出的方法显示出高精度和时间效率，并优于其他鲁棒配准方法。