Markov localization accommodates the possibility of ambiguous position estimates that may later be resolved by additional measurements. Suggest two examples of how that can arise in practice both...

Markov localization accommodates the possibility of ambiguous position estimates that may later be resolved by additional measurements. Suggest two examples of how that can arise in practice both indoors and outdoors. The method is based on an assumption that sensor error can be modelled with a Gaussian distribution. Suggest two instances where this assumption is violated