  This paper proposes a Simultaneous Calibration and Navigation (SCAN)
algorithm of a multiple Ultrasonic Local Positioning Systems (ULPSs) that cover
an extensive indoor area. The idea is the development of the same concept than
SLAM (Simultaneous Localization and Mapping), in which a Mobile Robot (MR)
estimates the map while it is navigating. The MR calibrates the beacons of
several ULPSs while it is moving inside the localization area. The concept of
calibration is the estimation of the position of the beacons referenced to a
known map. The scenario is composed of some calibrated ULPSs that we denote as
Globally Referenced Ultrasonic Local Positioning Systems (GRULPSs) that are
located in strategic points like entrances covering the start and the end of a
possible trajectory in the environment. Additionally, there are several
non-calibrated ULPSs named Locally Referenced Ultrasonic Local Positioning
Systems (LRULPSs) that are placed around the localization area. The proposal
uses a MR with odometer for calibrating the beacons of the LRULPSs while it is
navigating on their coverage area and go from one GRULPS to another. The
algorithm is based on multiple filters running in parallel (one filter for each
LRULPS and another one for the GRULPSs) that estimate the global and local
trajectories of the MR (one trajectory for each local reference system of the
LRULPSs) fusing the information related to the Ultrasound Signals (US) and the
odometer of the MR. The position of the beacons of the LRULPSs are obtained by
a transformation vector for each LRULPS that converts the local coordinates to
the global reference system. Extended Kalman Filter (EKF), Unscented Kalman
Filter (UKF) and H-Inf Filter have been tested, in simulations and real
experiments, in order to compare their performance in this case.
