Ground segmentation and free space estimation in off-road terrain

In this paper, we propose a novel approach for ground segmentation and free space estimation of outdoor environments. The system is completely self-supervised and relies on two modules: the first module is built around a Fully Convolutional Network (FCN), and is used for ground segmentation after the system is initiated. The second module relies on depth information paired with interactive graphs cuts, and is used to train the FCN at startup, and anytime the FCN's performance degrades during runtime. This usually happens when the camera observes a new type of outdoor scene, which is foreign to the FCN. Experiments were conducted on three datasets of different ruggedness to highlight the advantages of the proposed method.