Determining the physical location is a fundamental challenge in location based services and service robotics. This paper presents an approach to simultaneously determine the pose of a mobile robot and the positions of static wireless nodes based on a new technique to compute the angle of arrival of radio signals. For the overall localization process an Extended Kalman Filter is employed. For the initialization of the nodes positions a Particle Filter is used, to overcome the nonlinearity problem. Experiments show a mean accuracy for the position of the robot of 39mm for an indoor office environment and 80mm for an outdoor landscape environment.

We present a new method for localization using received signal strength indicator (RSSI) in ordinary wireless communication networks such as specified by IEEE 802.15.4. The method exploits the anisotropy of the antenna gain to determine the bearing of the robot relative to reference radio nodes. This method is not only more precise than the mapping of the RSSI to distance only, it also allows to estimate the orientation of the robot and to monitor the integrity of the measurement. The integrity measure is also incorporated into the RSSI to distance mapping and a thorough error analysis is presented. The paper describes the localization concept and presents experimental results for mobile robot localization in an outdoor environment. The achieved accuracy is significantly increased compared to previously developed RSSI based localization methods.

This paper deals with localization of a mobile robot using received signal strength indicator (RSSI) in low power IEEE 802.15.4 conform wireless communication in an outdoor environment. Hardware modifications are derived to reduce the radio irregularity and to increase the uniqueness of the measured RSSI to distance. A novel algorithm is elaborated allowing for sub meter accuracy. It accounts for the noise and implicitly models the uncertainty. Additionally it is robust to node failures. To further improve the accuracy a particle filter is employed to perform probabilistic sensor fusion of odometry, ultrasonic and RSSI sensors and a map. The suitability of this approach is shown with real measurements achieving a mean positioning error of 0.32 m.