Comparing ICP variants on real-world data sets
Comparing ICP variants on real-world data sets
复制标题
DOI：
10.1007/s10514-013-9327-2
复制
发表时间：
2013-04-01
影响因子：
3.5
通讯作者：
Magnenat, Stephane
中科院分区：
文献类型：
--
作者：
Pomerleau, Francois;Colas, Francis;Magnenat, Stephane
许多用于测绘的现代传感器产生3D点云，这些点云通常使用迭代最近点（ICP）算法配准在一起。由于ICP有许多变体，其性能取决于环境和传感器，因此已发布了数百种变体。然而，没有可用的比较框架，导致难以为特定的实验条件选择合适的变量。本文件的第一个贡献包括一个协议，允许比较比较方案的变量，考虑到广泛的投入。第二个贡献是一个开源的ICP库，它足够快，可以在多个现实世界的应用程序中使用，同时足够模块化，便于比较多个解决方案。本文介绍了这些领域的应用程序的两个例子。最后一项贡献是使用涵盖各种环境的数据集比较方案的两个基线变量。除了证明需要改进比较方案方法以适应自然、非结构化和缺乏信息的环境外，这些基线变量还为比较新的解决方案提供了坚实的基础。我们的协议、软件和基线结果的结合令人信服地证明了开源软件如何推动地图和导航研究。
Many modern sensors used for mapping produce 3D point clouds, which are typically registered together using the iterative closest point (ICP) algorithm. Because ICP has many variants whose performances depend on the environment and the sensor, hundreds of variations have been published. However, no comparison frameworks are available, leading to an arduous selection of an appropriate variant for particular experimental conditions. The first contribution of this paper consists of a protocol that allows for a comparison between ICP variants, taking into account a broad range of inputs. The second contribution is an open-source ICP library, which is fast enough to be usable in multiple real-world applications, while being modular enough to ease comparison of multiple solutions. This paper presents two examples of these field applications. The last contribution is the comparison of two baseline ICP variants using data sets that cover a rich variety of environments. Besides demonstrating the need for improved ICP methods for natural, unstructured and information-deprived environments, these baseline variants also provide a solid basis to which novel solutions could be compared. The combination of our protocol, software, and baseline results demonstrate convincingly how open-source software can push forward the research in mapping and navigation.