  Creating accurate spatial representations that take into account uncertainty
is critical for autonomous robots to safely navigate in unstructured
environments. Although recent LIDAR based mapping techniques can produce robust
occupancy maps, learning the parameters of such models demand considerable
computational time, discouraging them from being used in real-time and
large-scale applications such as autonomous driving. Recognizing the fact that
real-world structures exhibit similar geometric features across a variety of
urban environments, in this paper, we argue that it is redundant to learn all
geometry dependent parameters from scratch. Instead, we propose a theoretical
framework building upon the theory of optimal transport to adapt model
parameters to account for changes in the environment, significantly amortizing
the training cost. Further, with the use of high-fidelity driving simulators
and real-world datasets, we demonstrate how parameters of 2D and 3D occupancy
maps can be automatically adapted to accord with local spatial changes. We
validate various domain adaptation paradigms through a series of experiments,
ranging from inter-domain feature transfer to simulation-to-real-world feature
transfer. Experiments verified the possibility of estimating parameters with a
negligible computational and memory cost, enabling large-scale probabilistic
mapping in urban environments.
