A Method for Spatial Information Acquisition and Image Analysis of A Robotic Arm Based on An Improved Stereo Vision Matching Algorithm

This work investigates the impact of advanced stereo image matching algorithms on spatial information acquisition and image analysis in robotic arm systems. Stereoscopic vision plays a crucial role in obtaining precise 3D spatial data required for tasks such as object recognition, manipulation, and navigation. The work provides an overview of the evolution of stereo image matching techniques, from traditional methods to recent advancements through the integration of deep learning and hybrid approaches. These innovative methods are discussed in the context of their application in robotic arms, with a focus on improving image analysis and overall system performance. The work also addresses challenges such as computational efficiency, real-time processing, and robustness in dynamic environments, and proposes suitable solutions. The research focuses on combining advanced stereo image matching algorithms with a spatial pipeline for calibration, correction, disparity-depth transformation, and pose estimation. Even on rough surfaces, in glare, and during rapid movements, this method significantly improves the efficiency of spatial data acquisition. Experimental results show that the proposed method outperforms traditional algorithms, achieving lower disparity errors, more accurate 3D reconstruction, and more stable real-time performance. Finally, this article outlines future research directions in stereoscopic image processing, including the combination of deep learning and hardware acceleration to improve performance in dynamic robotic environments.