GMCR：用于点云配准的基于图的最大共识估计
GMCR: Graph-based Maximum Consensus Estimation for Point Cloud Registration
AI总结：
针对点云配准中高外点率导致最大共识估计NP难的问题，提出GMCR，将共识目标映射为图上最大团并闭式求解位姿，在多个基准上实现高精度与高效率。
摘要：
点云配准是自主机器人在非结构化环境中交互时的一项基础性且具有挑战性的问题，应用于物体位姿估计、同步定位与地图构建、机器人-传感器标定等场景。在基于全局对应的点云配准中，数据关联是一项极易出错的任务，通常会产生大量外点。若未能剔除外点，误差可能传播至下游感知任务。最大共识（MC）是一种广泛用于鲁棒估计的技术，但众所周知它是NP难问题。精确方法难以扩展到现实问题实例，而高外点率对近似方法也构成挑战。为此，我们提出了基于图的最大共识配准（GMCR），它对外点具有很强的鲁棒性，并可扩展到现实问题实例。我们提出了新的共识函数，将解耦的MC目标映射到图域，在该域中以最大团的形式找到最大共识集的紧近似。最终位姿估计以闭式形式给出。我们在合成配准基准、机器人物体定位任务以及扫描匹配基准上对所提出的GMCR进行了广泛评估。与其他最先进的MC方法相比，所提方法展现出较高的精度和时间效率，并且相较其他鲁棒配准方法也具有优势。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
