  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.
