  We present a novel method for reliable robot navigation in uneven outdoor
terrains. Our approach employs a novel fully-trained Deep Reinforcement
Learning (DRL) network that uses elevation maps of the environment, robot pose,
and goal as inputs to compute an attention mask of the environment. The
attention mask is used to identify reduced stability regions in the elevation
map and is computed using channel and spatial attention modules and a novel
reward function. We continuously compute and update a navigation cost-map that
encodes the elevation information or the level-of-flatness of the terrain using
the attention mask. We then generate locally least-cost waypoints on the
cost-map and compute the final dynamically feasible trajectory using another
DRL-based method. Our approach guarantees safe, locally least-cost paths and
dynamically feasible robot velocities in uneven terrains. We observe an
increase of 35.18% in terms of success rate and, a decrease of 26.14% in the
cumulative elevation gradient of the robot's trajectory compared to prior
navigation methods in high-elevation regions. We evaluate our method on a Husky
robot in real-world uneven terrains (~ 4m of elevation gain) and demonstrate
its benefits.
