ARNL Localization

From MobileRobots Research and Academic Customer Support

Jump to: navigation, search
(TODO: MOGS and SONARNL information needs to be put in seperate pages.)

The exact methods are proprietary, but are based on algorithms shown to be reliable and efficient by past robotics research, and which are described in many robotics textbooks and published papers.

Currently, a majority of MobileRobot robots equipped with the laser range finder localize themselves in a map by merging the robot odometry with the laser readings using the Monte-Carlo localization (MCL) algorithm. This is done by the ArLocalizationTask in the ARNL library. Robots equipped with sonar also localize by merging their odometry with the sonar data based on the MCL. This is done by the ArSonarLocalizationTask in the SONARNL library. Robots such as the SEEKUR and the P3AT equipped with GPS sensors are localized in East-North-Up (ENU) coordinates using an extended Kalman filter which merges the odometry, the IMU and the GPS data. This localization is done by the ArGPSLocalizationTask in the MOGS library.

Despite the varied localization techniques, all localizations work with a known map of the environment which contain the relevant features. These maps are almost always generated by driving the robot around it and recording the sensor data. MobileRobot's MobilePlanner converts the data it collected during that procedure (such as laserscans) to build a map with all the relevant markers. The most common localization markers are the points and lines seen in the environment along with the location of the reflectors for ARNL. The localization in SONARNL uses just the lines in the map. The localization in MOGS uses the location of the orgin in lattitude longitude and altitude (LLA) in GPS coordinates.

One of the classic papers on Monte-carlo localization is D. Fox, W. Burgard, F. Dellaert, and S. Thrun. Monte carlo localization: Efficient position estimation for mobile robots. in Proceedings of the National Conference on Artificial Intelligence 1999 (pp 343–349). Probabilistic approaches in general to localization are usually covered in textbooks on autonomous mobile robotics as well, such as http://mitpress.mit.edu/books/introduction-autonomous-mobile-robots or http://www.probabilistic-robotics.org/.


See Also

Personal tools