EVIU-Link: Composite Collaborative Perception Framework for Enhanced Indoor Localization

Visual-inertial odometry (VIO) is widely used for mobile robot localization in indoor areas. Recent studies show that integrating global sensors such as ultra-wideband (UWB) into VIO can improve long-term robustness, but existing multi-sensor fusion schemes still face challenges. First, visual feature tracking, a core component of VIO, is easily degraded under low-texture, low-light, or fast-motion conditions, where current visual enhancement methods remain ineffective. Second, most fusion strategies are constrained to a single coupling paradigm (loose/tight) and a single estimation scheme (filter/optimization), which makes it difficult to jointly balance accuracy, efficiency, and robustness. To address these issues, we propose EVIU-link, an event-based VIO-UWB fusion framework. The key innovations are twofold: (1) an event-enhanced vision module, where feature-density monitoring dynamically triggers event-flow tracking to maintain robust feature matching under challenging visual conditions; and (2) a hierarchical fusion strategy that performs local pose estimation within a VIO-UWB constrained factor graph, while simultaneously applying UWB ranging constraints at the global level to correct accumulated drift. Experiments on public datasets and real-world indoor scenarios demonstrate that EVIU-link achieves superior performance in challenging environments, indicating its promising application potential.