@article{WeissGloesekoetterPrestesetal.2019,
  author    = {Weiss, Robin and Gl{\"o}sek{\"o}tter, Peter and Prestes, Edson and Kolberg, Mariana},
  title     = {Hybridisation of Sequential Monte Carlo Simulation with Non-linear Bounded-error State Estimation Applied to Global Localisation of Mobile Robots},
  series = {Journal of Intelligent \& Robotic Systems},
  journal   = {Journal of Intelligent \& Robotic Systems},
  editor    = {Springer,},
  doi       = {10.1007/s10846-019-01118-7},
  pages     = {1 -- 23},
  year      = {2019},
  abstract  = {Accurate self-localisation is a fundamental ability of any mobile robot. In Monte Carlo localisation, a probability distribution over a space of possible hypotheses accommodates the inherent uncertainty in the position estimate, whereas bounded-error localisation provides a region that is guaranteed to contain the robot. However, this guarantee is accompanied by a constant probability over the confined region and therefore the information yield may not be sufficient for certain practical applications. Four hybrid localisation algorithms are proposed, combining probabilistic filtering with non-linear bounded-error state estimation based on interval analysis. A forward-backward contractor and the Set Inverter via Interval Analysis are hybridised with a bootstrap filter and an unscented particle filter, respectively. The four algorithms are applied to global localisation of an underwater robot, using simulated distance measurements to distinguishable landmarks. As opposed to previous hybrid methods found in the literature, the bounded-error state estimate is not maintained throughout the whole estimation process. Instead, it is only computed once in the beginning, when solving the wake-up robot problem, and after kidnapping of the robot, which drastically reduces the computational cost when compared to the existing algorithms. It is shown that the novel algorithms can solve the wake-up robot problem as well as the kidnapped robot problem more accurately than the two conventional probabilistic filters.},
  language  = {en}
}