REFLoc: A Resilient Evolutionary Fusion Framework for Robust Indoor Localization

Location awareness is becoming increasingly crucial in unmanned aerial vehicles (UAVs). However, in the absence of global positioning system (GPS) signals in indoor scenes, how to obtain accurate positioning results has become a frontier issue that has attracted much attention. This paper proposes a resilient evolutionary multisensor fusion localization framework named "REFLoc" to achieve accurate indoor positioning of UAVs. Motivated by the ease of deployment and the wide feasibility of precise indoor localization, we introduce ultra-wideband (UWB) as global localization. The global localization provides the absolute position information of the UAV indoors, but it may be affected by signal blocking, occlusion, or interference. To address these noise effects, we introduce a hybrid threshold wavelet denoising (HTWD) algorithm to effectively preprocess UWB data. We also integrate visual inertial odometry (VIO) as the support of local tracking to provide continuous and high-frequency motion estimation. We dynamically adjust the weight parameters and noise covariance matrices by using the resilient evolutionary fusion method. Then we further seamlessly integrate the global localization and local tracking results in the optimized EKF framework. This improves the robustness of the system to environmental changes and outliers. We evaluate the performance of REFLoc in public datasets and in real world, and the experimental results show that the proposed framework can effectively improve the robustness and accuracy of the UAVs' localization.