Scene-Adaptive Off-Road Detection Using a Monocular Camera

This paper studies vision-based road detection for a robot's path following in off-road environments. We define the problem as detecting the region in front of the robot that is mechanically traversable (i.e., mechanical traversability), that is apt to be chosen by a human to drive through (i.e., human selection), and that extends for a distance to show the road's direction, shape, or even network of the intersection ahead (i.e., far-field capability). An algorithm framework is designed that contains two parts: inference and learning. In inference, the problem is formulated as a consecutive road type classification and road region segmentation to address the diversity of terrain surfaces. In model learning, the robot is first driven by a human being, with image samples on the track of the robot being collected that meet the prerequisites of both mechanical traversability and human selection. Evaluation measures are defined to examine the three requirements of mechanical traversability, human selection, and far-field capability. The performances of the above aspects are demonstrated on a data set using LiDAR, track and manual references, which will be released together with this publication.