  Navigation of a mobile robot is conditioned on the knowledge of its pose. In
observer-based localisation configurations its initial pose may not be knowable
in advance, leading to the need of its estimation. Solutions to the problem of
global localisation are either robust against noise and environment
arbitrariness but require motion and time, which may (need to) be economised
on, or require minimal estimation time but assume environmental structure, may
be sensitive to noise, and demand preprocessing and tuning. This article
proposes a method that retains the strengths and avoids the weaknesses of the
two approaches. The method leverages properties of the Cumulative Absolute
Error per Ray (CAER) metric with respect to the errors of pose hypotheses of a
2D LIDAR sensor, and utilises scan--to--map-scan matching for fine(r) pose
estimations. A large number of tests, in real and simulated conditions,
involving disparate environments and sensor properties, illustrate that the
proposed method outperforms state-of-the-art methods of both classes of
solutions in terms of pose discovery rate and execution time. The source code
is available for download.
