Aiming at the problems of low matching accuracy and poor real-time performance of existing image stitching algorithms in complex scenes, this paper proposes an image stitching algorithm based on improved ORB and MLESAC. In traditional image stitching approaches, feature detection exhibits insufficient robustness, and descriptors lack discriminative power under conditions of abrupt illumination changes, viewpoint variations or complex background interference. This deficiency readily induces mismatching errors, ultimately leading to stitching misalignments or ghosting artifacts.Thus, in the preprocessing stage of this paper, the input image is transformed into CIE Lab color space to decompose brightness and color channels, and an adaptive image pyramid is constructed by integrating information entropy with illumination statistics.In the feature detection and description stage, a lighting-adaptive FAST corner threshold adjustment mechanism is designed. Subsequently, local geometric constraints are introduced to filter corner points, and the BRIEF descriptor is extended to the L, a and b channels of the CIE Lab color space, thereby fusing local gradient direction information.In the feature matching stage, bidirectional Hamming distance matching is employed to establish a local-global constraint optimization framework for minimizing reprojection error. Subsequently, a more efficient MLESAC algorithm is employed to remove incorrect matches.Finally, a weighted average method is adopted to smooth the stitching area, achieving a seamless stitching effect.Experimental results demonstrate that the proposed algorithm can guarantee real-time performance and high-precision panoramic stitching quality when processing image stitching tasks in complex scenes. Specifically, on the APAP Dataset, the algorithm achieved a matching accuracy of 97.63%.