We present an efficient algorithm for complete motion planning that combines approximate cell decomposition (ACD) with probabilistic roadmaps (PRM). Our approach uses ACD to subdivide the configuration space into cells and computes localized roadmaps by generating samples within these cells. We augment the connectivity graph for adjacent cells in ACD with pseudo-free edges that are computed based on localized roadmaps. These roadmaps are used to capture the connectivity of free space and guide the adaptive subdivision algorithm. At the same time, we use cell decomposition to check for path non-existence and generate samples in narrow passages. Overall, our hybrid algorithm combines the efficiency of PRM methods with the completeness of ACD-based algorithms. We have implemented our algorithm on 3-DOF and 4-DOF robots. We demonstrate its performance on planning scenarios with narrow passages or no collision-free paths. In practice, we observe up to 10 times improvement in performance over prior complete motion planning algorithms.