Uncertain Geometric Information
Successful operation of a mobile robot in the navigation area highly depends on the environmental information gathered by its exteroceptive sensors such as laser rangefinder, ultrasonic sensors, CCD cameras, etc. Typically, geometric information derives from the surface of objects in the environment, such as planes, edges, vertices, etc. In general, only partial and imprecise information can be obtained from real sensors, thus integration of multiple observations of the same geometric entity is required to compute an estimation of its location with respect to the vehicle. Partiality refers to the degrees of freedom associated to the representation of different geometric entities and how they determine the location of other entities related to them. Imprecision refers to the accuracy in the estimation of the location of geometric entities. Adequately representing such environmental information is a crucial aspect in mobile robotics. Dealing with uncertain geometric information has given rise to a variety of models which can be classified into either set-based models or probabilistic-models. On the one hand, set-based models describe the imprecision in the localization of a geometric entity by a bounded region, which correspond to the set of feasible locations for the geometric entity. Fusion of geometric information is performed using algebraic operations on the regions limited by the error bounds. On the other hand, probabilistic models represent the uncertain location of a geometric element using a probability distribution, usually Gaussian. Here, fusion is carried out using estimation methods.
KeywordsState Vector Mobile Robot Mahalanobis Distance Extend Kalman Filter Measurement Equation
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