Multi-camera wide field-of-view compound eye imaging system

With the rapid development of security surveillance and autonomous driving, the demand for wide field-of-view imaging is increasingly growing. Traditional wide field-of-view (FOV) single-camera systems often suffer from significant distortion, making it challenging to achieve comprehensive environmental perception. Compound eyes, with tightly packed multiple sub-eyes, have the advantages of being compact, lightweight, and possessing a wide FOV with high sensitivity to moving targets. This paper analyzes the characteristics of wide FOV imaging of biomimetic compound eyes and integrates the imaging features of multiple cameras to design a multi-camera imaging system based on a biomimetic compound eye imaging system. The proposed system comprises nine cameras with a resolution of 640×480 arranged in a spherical multi-row configuration to emulate the compound eye structure. Given that feature matching significantly impacts the effectiveness of video stitching, we analyze and compare existing typical feature-matching algorithms from the aspects of both matching accuracy and robustness. Consequently, we employ the LightGlue on top of SuperPoint features for feature point matching to ensure the accuracy of subsequent video stitching. Through imaging experiments, the results demonstrate that this video stitching algorithm achieves satisfactory stitching results with minimal mismatches. The proposed multi-camera wide field-of-view compound eye imaging can realize wide wide-field imaging of 170°×120°with 8 frames per second.