Accurate and robust localization is a critical component in intelligent vehicles, playing a significant role in route planning and efficient navigation. There is a rising trend towards affordable positioning solutions that use common vehicular sensors like GPS, IMU, and cameras to improve navigation accuracy. This paper presents a comprehensive, low-cost localization framework with a lightweight map. The framework introduces two key novelties. Firstly, we propose a method known as the Cross-Dimensional Lane and Pose Estimator (CDLPE), designed to effectively resist scenarios with poor satellite signals. Additionally, our system delivers a reliable localization service by effectively integrating matching results and capitalizing on the benefits of the sensors used, coupled with the understanding of the environment. We have verified the robustness of our method under different driving scenarios. Compared to the classical Iterative Closest Point (ICP) algorithm, the lane identification accuracy has improved by 4.42% and 9.23% during normal and weak satellite signal conditions, respectively. Videos in: https://youtu.be/DsYXSeWQhWc .