Registration of terrestrial point clouds is essential for large-scale urban applications. The robustness, accuracy, and runtime are generally given the highest priority in the design of appropriate algorithms. Most approaches that target general scenarios can only fulfill some of these factors, that is, robustness and accuracy come at the cost of increased runtime and vice versa. This paper proposes an object-based incremental registration strategy that accomplishes all of these objectives without the need for artificial targets, aiming at a specific scenario, the urban environment. The key is to decompose the degrees of freedom for the SE(3) transformation to three separate but closely related steps, considering that scanners are generally leveled in urban scenes: (1) 2D transformation with matches from line primitives, (2) vertical offset compensation by robust least-squares optimization, and (3) full SE(3) least-squares refinement using uniformly selected local patches. The robustness is prioritized in the whole pipeline, as structured first by a primitive-based registration and two least-squares optimizations with robust estimations that do not require specific keypoints. An object-based strategy for terrestrial point clouds is used to increase the reliability of the first step by the line primitives, which significantly reduces the search space without affecting the recall ratio. The least-squares optimization contributes to achieve a global optimum for the accurate registration. The three coupling steps are also more efficient than segregated coarse-to-fine registration. Experimental evaluations for point clouds acquired in both a metropolis and in old-style cities reveal that the proposed methods are superior to or on par with the state-of-the-art in robustness, accuracy, and runtime. In addition, the methods are also agnostic to the primitives adopted.

地面点云的配准对于大规模城市应用至关重要。在适当算法的设计中，通常将鲁棒性，准确性和运行时间放在首位。针对一般场景的大多数方法只能满足其中一些因素，也就是说，鲁棒性和准确性是以增加运行时间为代价的，反之亦然。本文提出了一种基于对象的增量注册策略，它针对特定的场景（城市环境）实现了所有这些目标，而无需人工目标。关键是将SE（3）转换的自由度分解为三个独立但密切相关的步骤，考虑到扫描仪通常在城市场景中处于水平状态：（1）带有线图元匹配的2D转换，（2）通过稳健的最小二乘法优化进行垂直偏移补偿，以及（3）使用均匀选择的局部色块进行完整的SE（3）最小二乘法优化。鲁棒性在整个流水线中被优先考虑，首先通过基于基元的注册和两个最小二乘优化（具有不需要特定关键点的鲁棒性估计）进行结构化。地面点云的基于对象的策略用于通过线图元来提高第一步的可靠性，从而在不影响查全率的情况下显着减少搜索空间。最小二乘优化有助于实现全局最优以实现精确配准。这三个耦合步骤也比分离的从粗到精配准更有效。在鲁棒性，准确性和运行时间方面与最新技术相当。此外，这些方法也与所采用的原语无关。