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 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 to train
an attention-based Graph Neural Network (GNN) model to predict trajectory
success probabilities. We further analyze the image and point cloud data
separately to quantify sensor 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