Typical algorithms for point cloud registration such as Iterative Closest Point (ICP) require a favorable initial transform estimate between two point clouds in order to perform a successful registration. State-of-the-art methods for choosing this starting condition rely on stochastic sampling or global optimization techniques such as branch and bound. In this work, we present a new method based on Bayesian optimization for finding the critical initial ICP transform. We provide three different configurations for our method which highlights the versatility of the algorithm to both find rapid results and refine them in situations where more runtime is available such as offline map building. Experiments are run on popular data sets and we show that our approach outperforms state-of-the-art methods when given similar computation time. Furthermore, it is compatible with other improvements to ICP, as it focuses solely on the selection of an initial transform, a starting point for all ICP-based methods.

翻译：点云配准的典型算法（如迭代最近点算法，ICP）需要两个点云之间良好的初始变换估计才能成功完成配准。选择这一初始条件的现有最优方法依赖于随机采样或分支定界等全局优化技术。本文提出一种基于贝叶斯优化的新方法，用于寻找关键的初始ICP变换。我们提供了三种不同的算法配置，既能在短时间内快速获得结果，也能在离线建图等具有更多运行时间的场景中进行精细化优化，充分体现了算法的通用性。在公开数据集上的实验表明，在相似计算时间下，我们的方法优于现有最优方法。此外，该方法仅专注于选择初始变换（所有ICP方法的起点），因此可与ICP的其他改进方案兼容。