A Semantic-Geomagnetic Combined Descriptor for Single-Frame Opposite-Viewpoint Visual Place Recognition

In practical applications, mobile robots often return to the same place from opposite directions at different times, resulting in opposite viewpoints and appearance changes. In this intricate environment, the successful generation of visual place recognition (VPR) is crucial for tasks, such as simultaneous localization and mapping (SLAM), geo-localization, and the navigation of mobile robots. Inspired by the successful migration of animals by combining magnetic field information, this article compactly encodes the categories and 3-D positions of visual semantic objects by combining the simple and reliable geomagnetic direction provided by geomagnetic sensors and generates lightweight single-frame image descriptors based on the geomagnetic direction and distance between semantic objects, so as to realize the opposite-viewpoint VPR task with only single-frame image information in the scene with changes in appearance. The performance of the proposed method is assessed in comparison to 14 cutting-edge VPR methods, employing three precision-recall metrics: area under curve (AUC), recall at 100% precision, and precision at 100% recall. The results indicate that while many other methods decline in performance, our approach consistently exhibits superior results in scenarios with significant viewpoint and appearance variations. In the test datasets, our method obtains the highest AUC value on average, which balances the precision and recall well. Additionally, the robustness and real-time performance of our method are evaluated, demonstrating consistently high levels in both aspects.