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A new concept named pseudo information measure is introduced. By this measure, Bayesian fusion of independent sources of information is extended to a wide range of possible formulations and some new fusion formulas are calculated. The coincidence between the performance of the proposed method of fusion with the results that are expected by human logic and output sensitivity of the fusion process are discussed. Also, we have discussed the resulting flexibility for map building applications. Map building by using the proposed fusion formulas has been implemented on Khepera robot. The resulting map were fed to "A *" path planning algorithm for comparative purposes. For the resulting routes, two factors are considered: length and a danger
measure which is an increasing function of
the least distance of the path to obstacles. The results show that by using the proposed fusion formulas, more informative maps of the environment are
obtained by which more appropriate routes are achieved. Based on the selected function, there is a trade-off between the length of the resulting routes and their safety. This flexibility lets us choose the right fusion function for different map building applications.