Aiming at the problem that high-orbit satellite target-pointing on-orbit autocollimation systems cannot achieve real-time light spot localization under limited computational and storage resources, this paper proposes a storage-free real-time localization method for autocollimator light spots based on a parallel pipeline architecture. By utilizing the decomposition characteristics of centroid calculation, a stepwise centroid computation method is designed: The row or column centroids are calculated first and then combined into a two-dimensional centroid, realizing storage-free computation of the light spot centroid and avoiding the global storage requirements for the original image data. A sliding correlation filtering method and its FPGA implementation scheme are developed based on the parallel pipeline architecture. A Gaussian negative second derivative template was used to effectively suppress background gradient noise and random noise through real-time correlation calculation between pixels in the sliding window and the template. Hardware optimization designs such as pipeline multipliers and additive tree accumulators were utilized to ensure synchronous filtering operations during data streaming input, reducing noise impact while ensuring real-time computation. The method was verified through simulations and actual hardware deployment. Results demonstrate that, under the premise of ensuring computational accuracy, the proposed method completes filtering and centroid calculation within 246 clock cycles after reading the light spot information, utilizing only a small amount of FPGA on-chip RAM. At a 25 MHz clock frequency, the computation time is only 9.84 μs, with an average deviation of 0.032 pixels, achieving both high precision and real-time capability. This method can significantly improve the real-time feedback of on-orbit monitoring data for optical payload line-of-sight pointing in high-orbit satellites, providing real-time data support for tracking and locating high-speed maneuvering targets, with important application value and prospects in aerospace remote sensing.