An Embedded Parallel-Accelerated UAV Localization System Compatible with Optical and Infrared Sensors

Scene matching-based localization systems (SMLSs) offer an effective solution to the failure of Global Navigation Satellite System (GNSS) positioning in complex environments. This paper designs and implements a vision-based autonomous localization system for unmanned aerial vehicles (UAVs), compatible with both optical and infrared sensors, delivering high frame rates and high-precision positioning performance. First, to address the issue of uneven texture distribution in natural terrain features, an adaptive expansion sliding window model is constructed to accurately extract texture-rich regions, which effectively improves matching precision. Second, considering the differences in edge characteristics between optical and infrared images, the Sobel operator and Scharr operator are introduced, respectively, to construct gradient features, achieving high-precision, high-frame-rate heterogeneous image matching. Furthermore, to significantly improve the system frame rate, this paper designs an embedded parallel acceleration strategy based on a multi-core CPU architecture. The strategy achieves task-level concurrency between the front-end stages (pre-correction and feature refinement) and matching, and implements parallel optimization for feature construction and correlation computation within the matching module. On the algorithmic level, the correlation computation is further accelerated by replacing spatial-domain convolution with frequency-domain multiplication. Finally, the algorithm is deployed on an RK3588 embedded platform. The effectiveness of the proposed system is validated using offline flight data from a medium-altitude fixed-wing UAV and real-time flight experiments with a low-altitude rotary-wing UAV. In the medium-altitude UAV flight data validation, optical visual localization achieves an average position error of 20.94 m with a processing time of 0.123 s/frame, while infrared visual localization yields a position error of 11.77 m at 0.128 s/frame. In the low-altitude UAV flight experiment, optical visual localization achieves an average position error of 9.68 m at 0.15 s/frame, and infrared visual localization achieves an average position error of 11.22 m at 0.15 s/frame.