Laser rangefinders are very popular sensors in robot localization due to their accuracy. Typically, localization algorithms based on these sensors compare range measurements with previously obtained maps of the environment. As many indoor environments are highly symmetrical (e.g., most rooms have the same layout and most corridors are very similar) these systems may fail to recognize one location from another, leading to slow convergence and even severe localization problems. To address these two issues we propose a novel system which incorporates WiFi-based localization into a typical Monte Carlo localization algorithm that primarily uses laser rangefinders. Our system is mainly composed of two modules other than the Monte Carlo localization algorithm. The first uses WiFi data in conjunction with the occupancy grid map of the environment to solve convergence of global localization fast and reliably. The second detects possible localization failures using a metric based on WiFi models. To test the feasibility of our system, we performed experiments in an office environment. Results show that our system allows fast convergence and can detect localization failures with minimum additional computation. We have also made all our datasets and software readily available online for the community.