Point cloud registration is a fundamental problem in 3D computer vision. This paper addresses scan matching for reliable Mobile LiDAR Systems (MLSs) that can traverse different environments and be robust against outliers and noise. High reliability in multiple scenarios is vital to many applications, such as autonomous driving, but poor feature representations often compromise it. This paper introduces an expressive feature called the rectangle-flattening representation to enhance reliability. First, we propose a clustering method based on density, direction and flattening that allows regions to grow in a “planes first, lines second, less flattened structures last” manner. This method can extract rectangles from environments where planes are scarce. Second, we develop a squared point-to-rectangle distance function that is piecewise yet continuously differentiable to leverage the rectangle-flattening representation for scan matching. Unlike the traditional point-to-plane or plane-to-plane residual functions that rely on planar surfaces in other directions to provide translational information, our point-to-rectangle distance function is intrinsically translation-aware.

Extensive experiments are conducted on three aspects: scan matching accuracy, robustness, and odometry and mapping on MLSs. We compare our algorithm to several state-of-the-art methods using KITTI and Ford datasets in scan matching accuracy test with environments covering residential areas, highways, rural areas, downtown areas and campuses. Rigorous experiments show that among all of the methods compared, only RectMatch has an overall scan matching success rate surpassing 90% and even 95% across the two datasets. The robustness tests demonstrate that RectMatch can better deal with random outliers and Gaussian noise. For a comprehensive evaluation of RectMatch for MLSs, the third test incorporates five publicly available datasets using different laser scanners on multiple platforms traversing different environments. The results show high algorithm reliability and accuracy.

点云配准是 3D 计算机视觉中的一个基本问题。本文讨论了可靠的移动 LiDAR 系统 (MLS) 的扫描匹配问题，该系统可以遍历不同的环境并对异常值和噪声具有鲁棒性。多种场景下的高可靠性对于自动驾驶等许多应用至关重要，但糟糕的特征表示往往会影响它。本文介绍了一种称为矩形展平表示的表达功能，以提高可靠性。首先，我们提出了一种基于密度、方向和扁平化的聚类方法，该方法允许区域以“先平面，第二线，最后扁平化结构”的方式生长。这种方法可以从平面稀缺的环境中提取矩形。第二，我们开发了一个平方点到矩形距离函数，它是分段但连续可微的，以利用矩形展平表示进行扫描匹配。与传统的点到平面或平面到平面残差函数依赖于其他方向的平面来提供平移信息不同，我们的点到矩形距离函数本质上是平移感知的。

在三个方面进行了广泛的实验：扫描匹配精度、鲁棒性以及 MLS 上的里程计和映射。我们将我们的算法与使用 KITTI 和 Ford 数据集的几种最先进的方法进行比较，以在涵盖住宅区、高速公路、农村地区、市区和校园的环境中进行扫描匹配精度测试。严格的实验表明，在所有比较的方法中，只有 RectMatch 在两个数据集上的整体扫描匹配成功率超过 90% 甚至 95%。稳健性测试表明 RectMatch 可以更好地处理随机异常值和高斯噪声。为了对 MLS 的 RectMatch 进行全面评估，第三个测试结合了五个公开可用的数据集，这些数据集在穿越不同环境的多个平台上使用不同的激光扫描仪。