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

Off-Road Drivable Area Extraction Using 3D LiDAR Data