Local world modelling for teleoperation in a nuclear environment using a Bayesian multiple hypothesis tree

The presented local world modelling tool assists a human operator of a teleoperated robot in a nuclear environment in locating and recognising stationary objects with an ultrasonic sensor. The interpretation of the measurements of this type of sensor is not trivial for a human operator. In addition, not only the uncertainty on the measurement value is important, but also the uncertainty on the origin of the measurement needs to be dealt with, i.e. from which feature of which object the measurement originates. The Bayesian multiple-hypothesis tree allows different hypotheses on the origin of a measurement to coexist if the ambiguity cannot be resolved at once. For each of these hypotheses, rigorous Bayesian methods are used to update the estimate of the object location, and to calculate its probability. The presented algorithm is easier to apply than other existing multiple hypothesis algorithms. The probability of each local scene model is calculated as the probability of this residual error of the estimate. It is shown that this residual error can be calculated in a trivial way from the results of the Kalman filter based estimator. In addition, the assumption of a static world allows to do without complex probability calculus