Edge cost measures for topological maps should be consistent with the actual difficulties experienced upon map edge traversal. This is a non-trivial problem for robots steered by a behaviour-based control subsystem, as little a priori information is available about the real trajectory emerging during motion. As solution, the paper proposes to learn consistent estimates of major cost factors for topological edges through a posteriori observation of situation assessments produced by the low-level control layer. These observations are then subjected to a spatial or temporal integration, yielding a multi-dimensional cost vector. Simulation results show that increasingly accurate cost estimates can indeed be derived using this strategy. This allows the use of the appealing abstract topological map representation for high-level navigation planning even in cluttered terrain, where consistent edge traversal cost estimates are indispensable for efficient path computation.