Robust Hybrid Interval-Probabilistic Approach for the Kidnapped Robot Problem

Abstract

For a mobile robot to operate in its environment it is crucial to determine its position with respect to an external reference frame using noisy sensor readings. A scenario in which the robot is moved to another position during its operation without being told, known as the kidnapped robot problem, complicates global localisation. In addition to that, sensor malfunction and external influences of the environment can cause unexpected errors, called outliers, that negatively affect the localisation process. This paper proposes a method based on the fusion of a particle filter with bounded-error localisation, which is able to deal with outliers in the measurement data. The application of our algorithm to solve the kidnapped robot problem using simulated data shows an improvement over conventional probabilistic filtering methods.

Interval Extended Kalman Filter—Application to Underwater Localization and Control

“…The idea of combining probabilities with interval-based methods to have a method which is both safe and accurate is not new. It is the objective of the box particle filter [9,10] and other filters such as [11,12], which combines particle filters with set-membership methods. It is also the objective of the interval Kalman filter [13], which add interval analysis to bound uncertainties in the Kalman Filter.…”