Spatial Constrained and Candidate Expanded Parallel Matching for Ultra-Low Delay Camera Pose Estimation

In fields like robotics and factory automation (FA), an ultra-low delay vision system has emerged as a critical tool. This system processes videos at an astonishing 1000 frames per second (fps), with each frame being processed within 1 millisecond (1-ms). Camera pose estimation plays a crucial role by providing real-time and accurate spatial information, enabling rapid response and precise control of production processes in such systems. Existing works mainly focus on general purpose use (60 fps) while less of the focus on specific use (1000 fps). This paper proposed an ultra-low delay camera pose estimation method designed to meet the stringent requirements of modern $F A$ systems. The spatial constrained and candidate expanded parallel matching is proposed for accelerating 3D-2D matching process. Spatial constrain utilizes the tiny movements between adjacent frames to shrink the search range to decrease the probability of matching conflicts. And candidate expansion increases the candidate matching points to guarantee the quality of matching between 2D and 3D spaces, which increases parallelism and enhances efficiency of the 3D-2D matching process. Experiments are conducted using a 1000 fps camera with resolution of $640 \times 360$. The proposed method demonstrated robust performance across horizontal, vertical, and random camera movements, achieving an average Relative Pose Error (RPE) of 0.5 cm for translation and 0.4 degrees for rotation respectively.