  Road detection or traversability analysis has been a key technique for a
mobile robot to traverse complex off-road scenes. The problem has been mainly
formulated in early works as a binary classification one, e.g. associating
pixels with road or non-road labels. Whereas understanding scenes with
fine-grained labels are needed for off-road robots, as scenes are very diverse,
and the various mechanical performance of off-road robots may lead to different
definitions of safe regions to traverse. How to define and annotate
fine-grained labels to achieve meaningful scene understanding for a robot to
traverse off-road is still an open question. This research proposes a
contrastive learning based method. With a set of human-annotated anchor
patches, a feature representation is learned to discriminate regions with
different traversability, a method of fine-grained semantic segmentation and
mapping is subsequently developed for off-road scene understanding. Experiments
are conducted on a dataset of three driving segments that represent very
diverse off-road scenes. An anchor accuracy of 89.8% is achieved by evaluating
the matching with human-annotated image patches in cross-scene validation.
Examined by associated 3D LiDAR data, the fine-grained segments of visual
images are demonstrated to have different levels of toughness and terrain
elevation, which represents their semantical meaningfulness. The resultant maps
contain both fine-grained labels and confidence values, providing rich
information to support a robot traversing complex off-road scenes.
