There has been a widespread study on multi-sensor integration to achieve precise and robust odometry for autonomous vehicles (AVs) in urban areas. LiDAR odometry and visual odometry can be affected by structureless scenarios and numerous dynamic objects. GNSS positioning can be degenerated due to the multipath and non-line-of-sight signals by buildings. Therefore, selecting appropriate weighing for heterogeneous sensors is a challenge for multi-sensor fusion. With the advancements in cellular vehicle-to-everything (C-V2X) and intelligent roadside units (RSUs), vehicles and the RSUs can collaborate to deliver reliable service. Inspired by this, this paper investigates continuous error maps for available sensors under different time conditions (noon, sunset, and night) to improve the positioning performance of surrounding AVs in complex urban environments. In particular, this paper presents an error-map-aided multi-sensor integrated system, which benefits from the error information collected by a sensor-rich AV. Then the error information is uploaded to the RSUs which is then distributed to the AVs. A smaller weight is assigned if a larger error is queried from the error map. To validate our approach, experiments were performed using the realistic CARLA simulator and our self-developed GNSS RUMS simulator. To benefit the research community, we open-sourced the implementation on our project page³³https://sites.google.com!view/v2x-cooperative-navigation..