Finding optimal path through a graph efficiently is central to many problems, including route planning for a mobile robot. BDD-based incremental heuristic search method uses heuristics to focus their search and reuses BDD-based information from previous searches to find solutions to series of similar search problems much faster than solving each search problem from scratch. In this paper, we apply BDD-based incremental heuristic search to robot navigation in unknown terrain, including goal-directed navigation in unknown terrain and mapping of unknown terrain. The resulting BDD-based dynamic A* (BDDD*) algorithm is capable of planning paths in unknown, partially known and changing environments in an efficient, optimal, and complete manner. We present properties about BDDD* and demonstrate experimentally the advantages of combining BDD-based incremental and heuristic search for the applications studied. We believe that our experimental results will make BDD-based D* like replanning algorithms more popular and enable robotics researchers to adapt them to additional applications.