Jointly Optimized Global-Local Visual Localization of UAVs

• URL: http://arxiv.org/abs/2310.08082v1
• Date: Thu, 12 Oct 2023 07:12:20 GMT
• Title: Jointly Optimized Global-Local Visual Localization of UAVs
• Authors: Haoling Li, Jiuniu Wang, Zhiwei Wei, Wenjia Xu
• Abstract summary: Navigation and localization of UAVs present a challenge when global navigation satellite systems (GNSS) are disrupted and unreliable. Existing visual localization methods achieve autonomous visual localization without error accumulation by matching with ortho satellite images. We propose a novel Global-Local Visual localization (GLVL) network, combining a large-scale retrieval module that finds similar regions with the UAV flight scene, and a fine-grained matching module that localizes the precise UAV coordinate. Our method achieves a localization error of only 2.39 meters in 0.48 seconds in a village scene with sparse texture features.
• Score: 17.83193033936859
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Navigation and localization of UAVs present a challenge when global navigation satellite systems (GNSS) are disrupted and unreliable. Traditional techniques, such as simultaneous localization and mapping (SLAM) and visual odometry (VO), exhibit certain limitations in furnishing absolute coordinates and mitigating error accumulation. Existing visual localization methods achieve autonomous visual localization without error accumulation by matching with ortho satellite images. However, doing so cannot guarantee real-time performance due to the complex matching process. To address these challenges, we propose a novel Global-Local Visual Localization (GLVL) network. Our GLVL network is a two-stage visual localization approach, combining a large-scale retrieval module that finds similar regions with the UAV flight scene, and a fine-grained matching module that localizes the precise UAV coordinate, enabling real-time and precise localization. The training process is jointly optimized in an end-to-end manner to further enhance the model capability. Experiments on six UAV flight scenes encompassing both texture-rich and texture-sparse regions demonstrate the ability of our model to achieve the real-time precise localization requirements of UAVs. Particularly, our method achieves a localization error of only 2.39 meters in 0.48 seconds in a village scene with sparse texture features.

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