  This paper contributes a novel and modularized learning-based method for
aerial robots navigating cluttered environments containing hard-to-perceive
thin obstacles without assuming access to a map or the full pose estimation of
the robot. The proposed solution builds upon a semantically-enhanced
Variational Autoencoder that is trained with both real-world and simulated
depth images to compress the input data, while preserving semantically-labeled
thin obstacles and handling invalid pixels in the depth sensor's output. This
compressed representation, in addition to the robot's partial state involving
its linear/angular velocities and its attitude are then utilized to train an
uncertainty-aware 3D Collision Prediction Network in simulation to predict
collision scores for candidate action sequences in a predefined motion
primitives library. A set of simulation and experimental studies in cluttered
environments with various sizes and types of obstacles, including multiple
hard-to-perceive thin objects, were conducted to evaluate the performance of
the proposed method and compare against an end-to-end trained baseline. The
results demonstrate the benefits of the proposed semantically-enhanced deep
collision prediction for learning-based autonomous navigation.
