A Hybrid Strategy for Achieving Robust Matching Inside the Binocular Vision of a Humanoid Robot
Binocular vision is a core module in humanoid robots, and stereo matching is one of the key challenges in binocular vision, relying on template matching techniques and mathematical optimization methods to achieve precise image matching. However, occlusion significantly affects matching accuracy and robustness in practical applications. To address this issue, we propose a novel hybrid matching strategy. This method does not require network training and has high computational efficiency, effectively addressing occlusion issues. First, we propose the Inverse Template Matching Mathematical Method (ITM), which is based on optimization theory. This method generates multiple new templates from the image to be matched using mathematical segmentation techniques and then matches them with the original template through an inverse optimization process, thereby effectively improving matching accuracy under mild occlusion conditions. Second, we propose the Iterative Matching Mathematical Method (IMM), which repeatedly executes ITM combined with optimization strategies to continuously refine the size of matching templates, thereby further improving matching accuracy under complex occlusion conditions. Concurrently, we adopt a local region selection strategy to selectively target areas related to occlusion regions for inverse optimization matching, significantly enhancing matching efficiency. Experimental results show that under severe occlusion conditions, the proposed method achieves a 93% improvement in accuracy compared to traditional template matching methods and a 37% improvement compared to methods based on convolutional neural networks (CNNs), reaching the current state of the art in the field. Our method introduces a reverse optimization paradigm into the field of template matching and provides an innovative mathematical solution to address occlusion issues.