Pseudo information measure: a new concept for extension of Bayesian fusion in robotic map building

Abstract 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 expected results 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 simulated for a cylindrical robot with eight ultrasonic sensors and implemented on Khepera robot. The resulting maps have been fed to an improved version of A* path planning for comparative purposes. For the resulting routes, two factors have been considered and calculated: length and the least distance to obstacles. The results show that the maps of the environment that are generated by using the proposed fusion formulas are more informative. In addition, 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.