This paper aims to evaluate the performance gains that can be obtained by introducing cooperative localization in an indoor firefighter localization system, through the use of scenariobased simulations. Robust and accurate indoor localization for firefighters is a problem that is not yet resolved. Foot-mounted inertial navigation systems (INS) have been examined for first responder localization, but they have an accumulating position error that grows over time. By using ultrawideband (UWB) ranging between the firefighters and combining range measurements with position and uncertainty estimates from the foot-mounted INS via a cooperative localization approach it is possible to reduce the position error significantly.An error model for the position estimates received from single and dual foot-mounted INS is proposed based on experimental results, and it contains a scaling error which depends on the distance travelled and a heading error which grows linearly over time. The position error for dead-reckoning systems depends on the type of movement. Similarly, an error model for the UWB range measurements was designed where the range measurements experience a bias and variance, which is determined by the number of walls between the transmitter and receiver.By implementing these error models in a scenario-based simulation environment it is possible to evaluate the performance gain of different cooperative localization algorithms. A centralized extended Kalman Filter (EKF) algorithm has been implemented, and the position accuracy and heading improvements are evaluated over a smoke diving operation scenario. The cooperative localization scheme reduces the position errors by up to 70% in a scenario where a three-person smoke diver team performs a search and rescue operation.
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Uncertainty Propagation
Our persistent experience fiom thb and earlier tests isThe docking problem is well defined in the coordinates that the weak part when using ~on-contact senring for of the pallet. Thus, we start by the equations for how the feedback in robots is the association problem. It should be uncertainties are transformed and propagates. mentioned that the resolution of a range camera is strongly distance dependent. One finding in the paper is
Abstract-The safe fusion algorithm is benchmarked against three other methods in distributed target tracking scenarios. Safe fusion is a fairly unknown method similarly to, e.g., covariance intersection, that can be used to fuse potentially dependent estimates without double counting data. This makes it suitable for distributed target tracking, where dependencies are often unknown or difficult to derive. The results show that safe fusion is a very competitive alternative in five evaluated scenarios, while at the same time easy to implement and compute compared to the other evaluated methods. Hence, safe fusion is an attractive alternative in track to track fusion systems.
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