Insects rely extensively on visual information for navigation, utilizing a combination of innate instincts and learned behaviors. The mushroom body (MB) has been identified as a key player in insect visual learning, with insects possessing bilateral MBs. However, the mechanisms underlying the interaction between the left and right MBs, and how innate and learned visual cues are integrated for accurate navigation, remain elusive. In this study, we employ a novel approach wherein visual models fusing MB and innate are initially provided with binocular (left and right) visual inputs, followed by the application of a bio-plausible vector-summation method to integrate binocular innate and learned (MB) visual guidance. We verify the efficacy of this integration method by reproducing recently published data from biologists, thus providing computational support for empirical findings. This research not only sheds light on the potential mechanism for integrating bilateral innate and learned visual cues in the insect brain but also offers insights that could inspire efficient solutions for information fusion in robotics.