Localization for mobile robot teams using maximum likelihood estimation
- 25 June 2003
- proceedings article
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- Vol. 1, 434-439
- https://doi.org/10.1109/irds.2002.1041428
Abstract
This paper describes a method for localizing the members of a mobile robot team, using only the robots themselves as landmarks. That is, we describe a method whereby each robot can determine the relative range, bearing and orientation of every other robot in the team, without the use of GPS, external landmarks, or instrumentation of the environment. Our method assumes that each robot is able to measure the relative pose of nearby robots, to- gether with changes in its own pose; using a combination of maximum likelihood estimation (MLE) and numerical optimization, we can subsequently infer the relative pose of every robot in the team. This paper describes the basic formalism, its practical implementation, and presents ex- perimental results obtained using a team of four mobile robots.Keywords
This publication has 10 references indexed in Scilit:
- Elastic correction of dead-reckoning errors in map buildingPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2002
- Mobile robot exploration and map-building with continuous localizationPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2002
- A solution to the simultaneous localization and map building (SLAM) problemIEEE Transactions on Robotics and Automation, 2001
- A Probabilistic On-Line Mapping Algorithm for Teams of Mobile RobotsThe International Journal of Robotics Research, 2001
- Learning globally consistent maps by relaxationPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2000
- An Experimental Study of a Cooperative Positioning SystemAutonomous Robots, 2000
- Markov Localization for Mobile Robots in Dynamic EnvironmentsJournal of Artificial Intelligence Research, 1999
- A Probabilistic Approach to Concurrent Mapping and Localization for Mobile RobotsMachine Learning, 1998
- Globally Consistent Range Scan Alignment for Environment MappingAutonomous Robots, 1997
- Mobile robot localization by tracking geometric beaconsIEEE Transactions on Robotics and Automation, 1991