Jointly Optimized Global-Local Visual Localization of UAVs
Haoling Li Jiuniu Wang Zhiwei Wei Wenjia Xu
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.