Research on Image Matching in Indoor Environments Based on Enhanced SURF and RANSAC Algorithms

Image matching stands as a pivotal research direction within the realm of computer vision, with extensive applications in target recognition, image stitching, and three-dimensional reconstruction. In the context of indoor environments, challenges such as variations in lighting, texture complexity, and occlusion pose significant hurdles to the robustness and precision of algorithms. Among traditional image matching algorithms, SIFT (Scale-Invariant Feature Transform) and SURF (Speeded-Up Robust Features) are two extensively researched and applied methodologies. The SURF algorithm, with its advantages of rapid computation and strong resistance to interference, has become a crucial tool in the field of image matching. Nonetheless, the accuracy and stability of SURF still require enhancement when dealing with complex scenes. RANSAC (Random Sample Consensus), an algorithm designed to eliminate erroneous match points, is frequently employed to improve the accuracy of image matching. Thus, investigating improvements to the SURF algorithm and integrating it with RANSAC to enhance the performance of image matching in indoor environments holds significant theoretical and practical value. This study, based on a thorough analysis of the SURF algorithm, proposes an image matching approach tailored for indoor environments by refining feature point extraction and matching methods and incorporating the RANSAC algorithm to remove erroneous matches, with experimental validation of its effectiveness.