Concurrent mapping and localization in mobile robots

First of all autonomous mobile robots have to localize their own positions to perform their tasks. By fusing the odometric data, measurements acquired from sensors and map features, it is possible to localize the robot position. Maps either can be given at the beginning of motion or can be built by a mobile robot while it wanders around the environment. In this situation it is necessary to build the map and use it simultaneously to localize the robot pose. In this research, it is aimed to localize the robot position and to map the environment simultaneously. Advanced Triangulation Based Fusion algorithm was used to build a feature-based map of the environment. Extended Kalman filter was used to reduce the errors based on odometric motion. In experimental work, Pioneer 3DX, an autonomous mobile robot with 16 sonars and 2 optical shaft encoders located on the wheels were used. The simulation-based applications were realized by using the MobileSim simulator which is prepared for Pioneer robots and Matlab program. After the additional two processes have been incorporated into Triangulation Based Fusion algorithm, it is succeed to remove the false edge points from the map and to enhance the performance of the algorithm. According to the initial conditions of EKF, the performance of localization was discussed. It is observed that last position error of the mobile robot is decreased with the use of EKF and ATBF together. The error between the real and the estimated robot positions during the motion was remained into the desired boundary range. This fact guarantees the consistency of EKF-based concurrent mapping and localization.KEY WORDS: Concurrent, Mapping, Localization, Triangulation Based Fusion, Feature Based Mapping, Extended Kalman Filter