Traditional real-time target localization methods lack a certain depth in extracting feature information from target images, resulting in significant positioning errors. Therefore, a real-time target localization method combining laser point cloud data and image segmentation is proposed. Based on the characteristics of laser point cloud data, the data is processed through point cloud down sampling, radius filter denoising, and other methods. The image of the positioning target is symmetrically segmented, and features with similar attributes are clustered. The positioning target features are deeply extracted, and image matching is performed using time index based on the extracted positioning target features. The density based DBSCAN algorithm is used to cluster the coordinate matching data of the positioning target, Form a clustered 3D point set, convert the 3D point set to obtain real-time positioning coordinates, and perform real-time positioning of the target. The experimental results show that this method has a small deviation in the real-time positioning process, with an error of within 2.0%, and is effective.