  We present a novel trajectory traversability estimation and planning
algorithm for robot navigation in complex outdoor environments. We incorporate
multimodal sensory inputs from an RGB camera, 3D LiDAR, and the robot's
odometry sensor to train a prediction model to estimate candidate trajectories'
success probabilities based on partially reliable multi-modal sensor
observations. We encode high-dimensional multi-modal sensory inputs to
low-dimensional feature vectors using encoder networks and represent them as a
connected graph. The graph is then used to train an attention-based Graph
Neural Network (GNN) to predict trajectory success probabilities. We further
analyze the number of features in the image (corners) and point cloud data
(edges and planes) separately to quantify their reliability to augment the
weights of the feature graph representation used in our GNN. During runtime,
our model utilizes multi-sensor inputs to predict the success probabilities of
the trajectories generated by a local planner to avoid potential collisions and
failures. Our algorithm demonstrates robust predictions when one or more sensor
modalities are unreliable or unavailable in complex outdoor environments. We
evaluate our algorithm's navigation performance using a Spot robot in
real-world outdoor environments. We observe an increase of 10-30% in terms of
navigation success rate and a 13-15% decrease in false positive estimations
compared to the state-of-the-art navigation methods.
