  We propose a robust and computationally efficient algorithm to generically
construct first return maps of dynamical systems from time series without the
need for embedding. Typically, a first return map is constructed using a
heuristic convenience (maxima or zero-crossings of the time series, for
example) or a computationally delicate geometric approach (explicitly
constructing a Poincar\'e section from a hyper-surface normal to the flow and
then interpolating to determine intersections with trajectories). Our approach
relies on ordinal partitions of the time series and builds the first return map
from successive intersections with particular ordinal sequences. Generically,
we can obtain distinct first return maps for each ordinal sequence. We define
entropy-based measures to guide our selection of the ordinal sequence for a
``good'' first return map and show that this method can robustly be applied to
time series from classical chaotic systems to extract the underlying first
return map dynamics. The results are shown on several well-known dynamical
systems (Lorenz, R{\"o}ssler and Mackey-Glass in chaotic regimes).
