The growing integration of distributed energy resources (DERs) in urban areas raises various reliability issues. To ensure robust distribution grid operation, grid monitoring tools are needed, where the topology reconstruction serves as the first step. However, the topology reconstruction is hard in distribution grid. This is because 1) the branches are difficult and expensive to monitor since most of them are underground in urban areas; and 2) the assumption of radial topology in many studies is inappropriate for meshed urban grids. To address these drawbacks, we propose a new data-driven approach to reconstruct distribution grid topology by utilizing the newly available smart meter data. Specifically, a graphical model is built to model the probabilistic relationships among different voltage measurements. With proof, the topology reconstruction problem is formulated as a regularized linear regression problem (Lasso) to deal with meshed network structures. Simulation results show highly accurate estimation in IEEE standard distribution test systems with and without loops using real smart meter data.