Global navigation satellite system (GNSS) is the standard solution for solving the localization problem in outdoor environments, but its signal might be lost when driving in dense urban areas or in the presence of heavy vegetation or overhanging canopies. Hence, there is a need for alternative or complementary localization methods for autonomous driving. In recent years, exteroceptive sensors have gained much attention due to significant improvements in accuracy and cost-effectiveness, especially for 3D range sensors. By registering two successive 3D scans, known as scan matching, it is possible to estimate the pose of a vehicle. This work aims to provide in-depth analysis and comparison of the state-of-the-art 3D scan matching approaches as a solution to the localization problem of autonomous vehicles. Eight techniques (deterministic and probabilistic) are investigated: iterative closest point (with three different embodiments), normal distribution transform, coherent point drift, Gaussian mixture model, support vector-parametrized Gaussian mixture and the particle filter implementation. They are demonstrated in long path trials in both urban and agricultural environments and compared in terms of accuracy and consistency. On the one hand, most of the techniques can be successfully used in urban scenarios with the probabilistic approaches that show the best accuracy. On the other hand, agricultural settings have proved to be more challenging with significant errors even in short distance trials due to the presence of featureless natural objects. The results and discussion of this work will provide a guide for selecting the most suitable method and will encourage building of improvements on the identified limitations.

中文翻译：

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用于城市和农业环境中自主机器人导航的3D扫描匹配技术的比较

全球导航卫星系统（GNSS）是解决室外环境中定位问题的标准解决方案，但是当在人口稠密的城市地区行驶，植被茂密或悬垂的檐篷行驶时，其信号可能会丢失。因此，需要用于自动驾驶的替代或补充定位方法。近年来，由于精确度和成本效率的显着提高，尤其在3D距离传感器方面，外感传感器得到了广泛的关注。通过注册两个连续的3D扫描（称为扫描匹配），可以估算车辆的姿态。这项工作旨在提供对最新3D扫描匹配方法的深入分析和比较，以解决自动驾驶汽车的定位问题。研究了八种技术（确定性和概率性）：迭代最近点（具有三个不同的实施例），正态分布变换，相干点漂移，高斯混合模型，支持矢量参数化的高斯混合和粒子滤波实现。它们在城市和农业环境的长距离试验中得到了证明，并在准确性和一致性方面进行了比较。一方面，大多数技术可以通过显示出最佳准确性的概率方法成功地在城市场景中使用。另一方面，由于无特征的自然物体的存在，即使在短距离试验中，农业环境也被证明更具挑战性且存在重大错误。