  Visual place classification from a first-person-view monocular RGB image is a
fundamental problem in long-term robot navigation. A difficulty arises from the
fact that RGB image classifiers are often vulnerable to spatial and appearance
changes and degrade due to domain shifts, such as seasonal, weather, and
lighting differences. To address this issue, multi-sensor fusion approaches
combining RGB and depth (D) (e.g., LIDAR, radar, stereo) have gained popularity
in recent years. Inspired by these efforts in multimodal RGB-D fusion, we
explore the use of pseudo-depth measurements from recently-developed techniques
of ``domain invariant" monocular depth estimation as an additional pseudo depth
modality, by reformulating the single-modal RGB image classification task as a
pseudo multi-modal RGB-D classification problem. Specifically, a practical,
fully self-supervised framework for training, appropriately processing, fusing,
and classifying these two modalities, RGB and pseudo-D, is described.
Experiments on challenging cross-domain scenarios using public NCLT datasets
validate effectiveness of the proposed framework.
