Double-layer fusion for cooperative localization was used combined with lidar in car and roadside binocular camera in order to achieve high-precision localization aiming at the problem of large localization error of unmanned vehicles in unstructured scenes. The down layer was two parallel pose estimations. Switching dual map was achieved through short-term and long-term estimation of pose error based on the adaptive Monte Carlo localization, and the cumulative error of scan matching for lidar was corrected. Kalman filter based on probability data association was used to eliminate non-detected targets’ interference for roadside cameras, and tracking was achieved. The upper layer fused pose estimations of two down layer as a global fusion estimation, and the result feedback was used to achieve autoregulation. The vehicle experiments showed that the localization accuracy of double-layer fusion cooperative localization was 0.199 m, and the yaw angle accuracy was 2.179°. It was greatly improved compared with localization by lidar on car or tight fusion without feedback. The localization accuracy can reach 7.8 cm as the number of roadside cameras increases.