From Satellite to Ground: Satellite Assisted Visual Localization with Cross-view Semantic Matching

One of the key challenges of visual Simultaneous Localization and Mapping (SLAM) in large-scale environments is how to effectively use global localization to correct the cumulative errors from long-term tracking. This challenge presents itself in two main aspects: first, the difficulty for robots in revisiting previous locations to perform loop closure, and second, the considerable memory resources required to maintain point-cloud-based global maps. Recent solutions have resorted into neural networks, using satellite images as the references for ground-level localization. However, most of these methods merely provide cross-view patch-matching results, which leads to unfeasible in integration with the SLAM system. To address these issues, we present a semantic-based cross-view localization method. This approach combines semantic information with a reward and penalty mechanism, enabling us to obtain a global probability map and achieve precise 3-degree-of-freedom (3-DoF) localization. Based on that, we develop a SLAM system that capitalizes on satellite imagery for global localization. This strategy effectively bridges the gap between SLAM and real-world coordinates while also substantially reducing accumulated errors. Our experimental results demonstrate that our global localization method significantly outperforms existing satellite-based systems. Moreover, in scenarios where the robot struggles to find loop closures, employing our localization method improves the SLAM accuracy.