A Multi-modal Approach to Single-modal Visual Place Classification
This addresses the challenge of robust long-term robot navigation for robotics applications, but it is incremental as it builds on existing multi-modal fusion approaches.
The paper tackles the problem of visual place classification from monocular RGB images, which is vulnerable to domain shifts like seasonal changes, by reformulating it as a pseudo multi-modal RGB-D classification using pseudo-depth from domain-invariant monocular depth estimation, achieving validated effectiveness in cross-domain scenarios on public NCLT datasets.
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.