  As mobile robots find increasing use in outdoor applications, designing
energy-efficient robot navigation algorithms is gaining importance. There are
two primary approaches to energy efficient navigation: Offline approaches rely
on a previously built energy map as input to a path planner. Obtaining energy
maps for large environments is challenging. Alternatively, the robot can
navigate in an online fashion and build the map as it navigates. Online
navigation in unknown environments with only local information is still a
challenging research problem. In this paper, we present a novel approach which
addresses both of these challenges. Our approach starts with a segmented aerial
image of the environment. We show that a coarse energy map can be built from
the segmentation. However, the absolute energy value for a specific terrain
type (e.g. grass) can vary across environments. Therefore, rather than using
this energy map directly, we use it to build the covariance function for a
Gaussian Process (GP) based representation of the environment. In the online
phase, energy measurements collected during navigation are used for estimating
energy profiles across the environment using GP regression. Coupled with an
A*-like navigation algorithm, we show in simulations that our approach
outperforms representative baseline approaches. We also present results from
field experiments which demonstrate the practical applicability of our method.
