Bio-inspired Vision Mapping and Localization Method Based on Reprojection Error Optimization and Asynchronous Kalman Fusion

Abstract

Bio-inspired visual systems have garnered significant attention in robotics owing to their energy efficiency, rapid dynamic response, and environmental adaptability. Among these, event cameras—bio-inspired sensors that asynchronously report pixel-level brightness changes called 'events', stand out because of their ability to capture dynamic changes with minimal energy consumption, making them suitable for challenging conditions, such as low light or high-speed motion. However, current mapping and localization methods for event cameras depend primarily on point and line features, which struggle in sparse or low-feature environments and are unsuitable for static or slow-motion scenarios. We addressed these challenges by proposing a bio-inspired vision mapping and localization method using active LED markers (ALMs) combined with reprojection error optimization and asynchronous Kalman fusion. Our approach replaces traditional features with ALMs, thereby enabling accurate tracking under dynamic and low-feature conditions. The global mapping accuracy significantly improved by minimizing the reprojection error, with corner errors reduced from 16.8 cm to 3.1 cm after 400 iterations. The asynchronous Kalman fusion of multiple camera pose estimations from ALMs ensures precise localization with a high temporal efficiency. This method achieved a mean translation error of 0.078 m and a rotational error of 5.411° while evaluating dynamic motion. In addition, the method supported an output rate of 4.5 kHz while maintaining high localization accuracy in UAV spiral flight experiments. These results demonstrate the potential of the proposed approach for real-time robot localization in challenging environments.