Three-dimensional (3D) maps are indispensable for autonomous robot navigation in outdoor environments. Loop closure, which is a key technology in robotic mapping, is essential for creating consistent maps. This paper proposes an efficient method of loop detection for 3D-mapping. The proposed method detects loop constraints by estimating robot poses in revisited places using point-cloud registration. A difficulty in the registration is a large set of 3D points obtained by laser sensors, which requires a long processing time. To reduce the processing time, the proposed method employs a coarse-to-fine approach. Coarse estimation is performed using planes, lines, and balls instead of 3D points, and reduces the hypothesized loop constraints using geometric constraints between the segments. Subsequently, fine estimation is performed using the iterative closest points (ICP) algorithm and 3D points. Another difficulty is the precision of loop detection. To increase the precision, the proposed method employs robustification techniques such as outlier removal in the registration, combination of feature-based and pose-based methods, and robust pose adjustment. Experiments using large-scale datasets show that the proposed method realizes realtime loop detection in a variety of outdoor environments including cities, parks, and forest areas. GRAPHICAL ABSTRACT

中文翻译：

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使用段组匹配的 3D LiDAR SLAM 环路检测

三维（3D）地图对于户外环境中的自主机器人导航是必不可少的。闭环是机器人制图的一项关键技术，对于创建一致的地图至关重要。本文提出了一种有效的 3D 映射循环检测方法。所提出的方法通过使用点云配准估计重访地点中的机器人姿态来检测循环约束。配准的一个难点是激光传感器获得的大量 3D 点，需要很长的处理时间。为了减少处理时间，所提出的方法采用从粗到细的方法。粗略估计是使用平面、线和球而不是 3D 点执行的，并使用段之间的几何约束来减少假设的循环约束。随后，使用迭代最近点 (ICP) 算法和 3D 点进行精细估计。另一个难点是环路检测的精度。为了提高精度，所提出的方法采用了鲁棒化技术，例如配准中的异常值去除、基于特征和基于姿态的方法的组合以及鲁棒姿态调整。使用大规模数据集的实验表明，所提出的方法在包括城市、公园和森林地区在内的各种户外环境中实现了实时环路检测。图形概要 和稳健的姿势调整。使用大规模数据集的实验表明，所提出的方法在包括城市、公园和森林地区在内的各种户外环境中实现了实时环路检测。图形概要 和稳健的姿势调整。使用大规模数据集的实验表明，所提出的方法在包括城市、公园和森林地区在内的各种户外环境中实现了实时环路检测。图形概要