基于FLoFTR算法的无人机实时在线地理定位

Real-time Geolocation on UAV Based on FLoFTR Algorithm

研制高效鲁棒的智能视觉定位方法是解决全球导航卫星系统(GNSS)拒止条件下无人机导航定位的重要途径之一。传统视觉定位方法存在精度较差、容易丢失定位的问题。本文提出一种FLoFTR算法,通过对高精度影像匹配算法LoFTR进行改进优化,在无人机计算平台上实现实时高精度定位。FLoFTR采用知识蒸馏方法压缩模型规模,提升推理效率,并通过改进特征提取模块和应用基于余弦距离的特征匹配方法,进一步降低了匹配时间并维持相当的匹配性能。在研制的软硬一体的平台上试验表明,优化后模型平均定位误差损失维持在0.1m以内,定位平均处理时间为47ms,定位速度提升超过7倍,可满足无人机定位的精度和实时性要求。

Developing an efficient and robust intelligent visual localization method is an effective means to solve the navigation and positioning of UAV under GNSS denial conditions.However,the accuracy of traditional visual localization method is poor,thus leading to lose location easily.By improving and optimizing the high-precision image matching algorithm LoFTR,this paper propose FLoFTR algorithm to achieve real-time localization on the UAV.FLoFTR uses knowledge distillation method to compress the model size and improve the inference efficiency.By improving the feature extraction module and applying the cosine distance,FLoFTR further reduces the matching time and maintains comparable matching performance.The experimental results show that the average localization error loss of the optimized model is less than 0.1m.And the average localization processing time is 47ms,which is increased by more than 7 times and can meet the precision and real-time requirements of UAV localization.