  We propose a method for off-road drivable area extraction using 3D LiDAR data
with the goal of autonomous driving application. A specific deep learning
framework is designed to deal with the ambiguous area, which is one of the main
challenges in the off-road environment. To reduce the considerable demand for
human-annotated data for network training, we utilize the information from vast
quantities of vehicle paths and auto-generated obstacle labels. Using these
autogenerated annotations, the proposed network can be trained using weakly
supervised or semi-supervised methods, which can achieve better performance
with fewer human annotations. The experiments on our dataset illustrate the
reasonability of our framework and the validity of our weakly and
semi-supervised methods.
