There are several ways to deal with state estimation in mobile robotics. The constraint programming approach consists of defining a problem as a set of rules and letting a solver perform the estimation. For mobile robotics, rules are constraints coming from state equations or uncertainties from the measurements.
Efforts have been done to propose operators and solvers to apply these constraints. The goal of this tutorial is to learn how to use them and understand the efficiency of the approach on realistic robotic applications. We will see that some problems that are difficult to solve with conventional methods (Kalman filters, particle approaches) can be easily dealt with by constraint programming. This is for instance the case of poor observation measurements, time uncertainties, delays, or when the initial conditions of the system are not known.
The tutorial will stand on the Tubex library, that provides tools for computations over sets of trajectories. It has been designed to deal with dynamical systems defined by non-linear differential equations and involving constraints such as trajectory evaluations, time uncertainties or delays. These computations stand on interval analysis, a well suited tool that reliably propagates uncertainties.
Organizers: Simon Rohou, Luc Jaulin, Benoît Desrochers, Raphael Voges
See the tutorial proposal