Accurately localize a mobile vehicle with an easy and quickly deployable system can be very useful for many applications. Herein we present an EKF-SLAM algorithm which allows using radio frequency (RF) beacons without any prior knowledge of their location. As RF beacons provide only range information, recovering their positions is not an easy task. For this range-only SLAM case, a new procedure to instantiate the beacons in the filter is proposed. The method uses two range measurements from different robot's positions to initialize two hypotheses for the beacon's location, which are then integrated in the filter as a Gaussian mixture. This approach is evaluated and compared to other initialization techniques in simulation and with a real dataset. The results show that our approach performs as well as the other existing methods for both trajectory and map errors with a low computational cost.

This work presents a new approach for mapping static beacons given only range measurements. An original formulation using sum-of-squares and linear matrix inequalities is derived to test if a measurement is inconsistent with a bounding box containing the beacon position. By performing this test for each range measurement, it is possible to recursively eliminate incompatible boxes and find the smallest consistent box. The box search is done with a breadth-first search algorithm that recursively prunes inconsistent boxes and splits the others to narrow the estimation. The validity of the method is asserted via simulations and compared to other standard mapping methods. Different levels and types of noise are added to evaluate the performances of the algorithm. It resulted that the approach accommodates very well classical zero-mean white Gaussian noises by adaptating the ratio of tolerated outliers for the consistency check, but fails to handle additive biases.

Abstract-To localize a mobile vehicle in a predefined area, it is possible to resort to a positioning system using beacons. The questions arising then are: what is the right number of beacons to deploy, and where should they be positioned to accurately localize the target. With a sensor placement algorithm, it is possible to generate a configuration of the beacons that optimize some quality criteria. However, designing the objectives to optimize is not simple, and the solution of the sensor placement algorithm may not always ensures a good localization in practice. This work evaluates the impact of the objectives of a sensor placement algorithm on different localization techniques. We show that criteria based on trilateration are not sufficient anymore when the localization is done with other techniques than trilateration. Indeed, data fusion-based localization algorithms use additional information such as odometry, and are less sensitive to poor coverage or poor beacon configurations than trilateration. Thus, designing new criteria taking into account the dynamics of the vehicle would probably improve further the placements and use less beacons for the same performance.