Paper discusses modern methods for localization of mobile ground robot in outdoor environment. Special attention is payed to lidar-based methods which allow to do precise simultaneous localization and mapping (SLAM) independently to lighting conditions. Modular approach to lidar-based localization is proposed. It consists of three modules: map reconstructor, map merger and corrector, localization module. As map reconstructor we use 3D Lidar-based Graph SLAM, state-of-the-art method LOAM and its modification with point cloud segmentation techniques A-LeGO-LOAM. As map merger and corrector we apply the technique of interactive map correction for 3D Graph SLAM. For localization module we compare different real-time approaches: NDT scan matching and Localization with scan batch processing. They do not allow to localize the robot with an unknown initial position. To overcome this problem, a stage of global Monte Carlo localization is proposed, which allows to generate a switching condition and a rough initial pose. Matrix of localization convergence speed is introduced to study influence of possible inaccuracies of initial pose estimation to the convergence of real-time localization approach. An important emphasis in the paper is made to the evaluation of the localization quality with relative and absolute metrics. Experimental results are obtained for own data from mobile ground robot ClearPath Husky equipped with Velodyne HDL-32E lidar. Proposed approach to robot localization achieves real-time performance for different hardware platforms in particular single-board computer Nvidia Jetson Xavier. It demonstrates possibility for integration of studied modular localization approach to onboard control systems of autonomous vehicles.