Probabilistic map building considering sensor visibility for mobile robot

This paper describes a method of probabilistic obstacle map building based on Bayesian estimation. Most active or passive obstacle sensors observe only the most frontal objects and any objects behind them are occluded. Since the observation of distant places includes large depth errors, a conventional method, which does not consider the sensor occlusion often, generate erroneous maps. We introduce a probabilistic observation model, which determines the visible objects. We first estimate probabilistic visibility from the current viewpoint by a Markov chain model based on the knowledge of the average sizes of obstacles and free areas. Then the likelihood of the observations based on the probabilistic visibility are estimated and then the posterior probability of each map grid are updated by Bayesian update rule. Experimental results show that more precise map building can be built by this method.