  Iterative Closest Point (ICP) is a widely used method for performing
scan-matching and registration. Being simple and robust method, it is still
computationally expensive and may be challenging to use in real-time
applications with limited resources on mobile platforms. In this paper we
propose novel effective method for acceleration of ICP which does not require
substantial modifications to the existing code.
  This method is based on an idea of Anderson acceleration which is an
iterative procedure for finding a fixed point of contractive mapping. The
latter is often faster than a standard Picard iteration, usually used in ICP
implementations. We show that ICP, being a fixed point problem, can be
significantly accelerated by this method enhanced by heuristics to improve
overall robustness. We implement proposed approach into Point Cloud Library
(PCL) and make it available online. Benchmarking on real-world data fully
supports our claims.
