Mapping for mobile robots integrates noisy spurious sensor data into a single coherent map useful for navigational purposes. There are various frameworks used for mapping, but the Bayesian framework appears to be most popular. In this paper, the theory behind the Bayesian framework as it is used in mapping is briefly compared to a framework based on evidential theory. The remainder of this paper evaluates the use of the evidential framework by simulating its use on a mobile robot with sparse range sensors. A sensor model is described for the range sensors to work with evidential mapping, and the framework was evaluated under varying parameters and in different simulated test environments.
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Yang et al. (2006) studied this question.
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