Conflict Management for Bayesian and DST Multi-Sensor Occupancy Grid Mapping
Lubos Vaci, Lauro Snidaro, Giuseppe Giorgio, Axel Furlan · 2018
Grid-based environment mapping and obstacle detection becomes increasingly more challenging when sensors' readings are highly contrasting. Without measurement prediction, commonly used approaches to grid fusion weight the sensor grids equally unless specified otherwise by the user. Empirically adjusted measurement weights are tailored only for certain scenarios and are not at all suited for a general purpose mapping. It therefore becomes apparent, that sensor weights need to be adjusted recursively during the map building process. We show that discrepancies between the grids can be exploited in such a manner where fusion of contradicting information will be less susceptible to sensor weighting and the accuracy of the mapped environment can be further improved. We present a realization of such a conflict resolution occupancy grid mapping, which combines grid-based mapping and situation assessment in a holistic approach.