Participant : François Le Gland [ corresponding person ] .
To illustrate that particle filtering algorithms are efficient, easy to implement, and extremely visual and intuitive by nature, for localisation, navigation and tracking problems in complex environments, with geometrical constraints, that would be very difficult to solve with usual Kalman filters. This material has proved very useful in training sessions and seminars that have been organized in response to the demand from industrial partners (SAGEM, CNES and EDF), and also in teaching. At the moment, the following three demos are available
Inertial position and velocity estimates are known to drift away from their true values, and need to be combined with some external source of information. In this demo, noisy measurements of the terrain height below an aircraft are obtained as the difference between (i) the aircraft altitude above the sea level (provided by a pression sensor) and (ii) the aircraft altitude above the terrain (provided by an altimetric radar), and are compared to the terrain height in any possible point (read on the elevation map). A cloud (swarm) of particles explores various possible trajectories generated from inertial navigation estimates and from a model of inertial navigation errors, and are replicated or discarded depending on whether the terrain height below the particle (i.e. at the same horizontal position) matches or not the available noisy measurement of the terrain height below the aircraft.
In this demo, several stations cooperate to locate and track a mobile from noisy angle measurements, in the presence of obstacles (walls, tunnels, etc), which make the mobile temporarily invisible from one or several stations.
In this demo, a mobile robot is finding its way inside a building, a digital map of which (including walls, doorways, etc.) is provided. The initial position, velocity and orientation of the robot are unknown, and noisy measurements of its rotation and linear displacement are given by an odometer. In addition, a ring of laser sensors detects with some error the distance from the robot to obstacles in sixteen different directions. A cloud (swarm) of particles explores various possible trajectories generated from odometer navigation estimates and from a model of odometer navigation errors, and are replicated or discarded depending on whether the distance from the particle to obstacles matches or not the available noisy measurement of the distance from the robot to the obstacles, in all sixteen directions, and depending also on whether the generated trajectories are compatible with the presence of obstacles.