  With the availability of many datasets tailored for autonomous driving in
real-world urban scenes, semantic segmentation for urban driving scenes
achieves significant progress. However, semantic segmentation for off-road,
unstructured environments is not widely studied. Directly applying existing
segmentation networks often results in performance degradation as they cannot
overcome intrinsic problems in such environments, such as illumination changes.
In this paper, a built-in memory module for semantic segmentation is proposed
to overcome these problems. The memory module stores significant
representations of training images as memory items. In addition to the encoder
embedding like items together, the proposed memory module is specifically
designed to cluster together instances of the same class even when there are
significant variances in embedded features. Therefore, it makes segmentation
networks better deal with unexpected illumination changes. A triplet loss is
used in training to minimize redundancy in storing discriminative
representations of the memory module. The proposed memory module is general so
that it can be adopted in a variety of networks. We conduct experiments on the
Robot Unstructured Ground Driving (RUGD) dataset and RELLIS dataset, which are
collected from off-road, unstructured natural environments. Experimental
results show that the proposed memory module improves the performance of
existing segmentation networks and contributes to capturing unclear objects
over various off-road, unstructured natural scenes with equivalent
computational cost and network parameters. As the proposed method can be
integrated into compact networks, it presents a viable approach for
resource-limited small autonomous platforms.

Computer Vision and Pattern Recognition;Robotics