Learning-based 3D Occupancy Prediction for Autonomous Navigation in Occluded Environments

In autonomous navigation, sensors suffer from massive occlusion in cluttered environments, leaving a significant amount of space unknown. In practice, treating the unknown space in optimistic or pessimistic ways both set limitations on planning performance. Therefore, aggressiveness and safety cannot be satisfied at the same time. Mimicking human behavior, in this paper, we propose a method based on deep neural network to predict occupancy distribution of unknown space. Specifically, the proposed method utilizes contextual information of environments and prior knowledge to predict obstacle distributions in the occluded space. Our self-supervised learning method use unlabeled and no-ground-truth data and augments the data by simulating navigation trajectories. Our Occupancy Prediction Network is faster than current SOTA scene completion models and is successfully applied to unseen test environments without any refinement. Results show that our predictor leverages the performance of a kinodynamic planner by improving security with no reduction of speed in cluttered environments.