Incorporating Network Connectivity in Beacon Localization via Cost Function Minimization

In robot localization systems, sensor networks are often deployed for computing the robots's positions. However, these systems require a prior knowledge of the positions of the beacons that from the sensor network. Range-only Simultaneous Localization and Mapping(SLAM) using robot-to-beacon range measurements has been on major approach to finding the positions of both the robot and the beacons in sensor networks. Other recent research has shown that by incorporating beacon-to-beacon ranges that establish the connectivity between beacons into the SLAM algorithm, the beacon map obtained by SLAM can be improved. In order to improve the map with such connectivity, a method for incorporating established connectivity is needed. Thus, a cost function minimization approach that considers the entire connectivity concurrently and has low computational complexity is proposed here. The simulation results show that this approach improves the accuracy of estimated positions for both the robot and the beacons.