The interactive museum tour-guide robot
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Communications of the ACM - Robots: intelligence, versatility, adaptivity
Proceedings of the 3rd ACM/IEEE international conference on Human robot interaction
Who will be the customer?: a social robot that anticipates people's behavior from their trajectories
UbiComp '08 Proceedings of the 10th international conference on Ubiquitous computing
Interactive robots as social partners and peer tutors for children: a field trial
Human-Computer Interaction
Field trial for simultaneous teleoperation of mobile social robots
Proceedings of the 4th ACM/IEEE international conference on Human robot interaction
Coordinated multi-robot exploration
IEEE Transactions on Robotics
Field trial of networked social robots in a shopping mall
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Comparison of laser-based person tracking at feet and upper-body height
KI'11 Proceedings of the 34th Annual German conference on Advances in artificial intelligence
How the Location of the Range Sensor Affects EKF-based Localization
Journal of Intelligent and Robotic Systems
Hi-index | 0.00 |
Robust localization of robots and reliable tracking of people are both critical requirements for the deployment of service robots in real-world environments. In crowded public spaces, occlusions can impede localization using on-board sensors. At the same time, teams of service robots working together need to share the locations of people and other robots on the same global coordinate system in order to provide services efficiently. To solve this problem, our approach is to use an infrastructure of sensors embedded in the environment to provide an inertial reference frame and wide-area coverage. Based on a people-tracking system we have previously established which uses laser range finders to track people's trajectories, we have developed a technique to localize a team of service robots on a shared global coordinate system. Each robot's odometry data is associated with the observed trajectory of an entity detected by the laser tracking system, and Kalman filters are used to correct rotational offsets between the robots' individual coordinate systems and the global reference frame. We present our data association and pose correction algorithms and show results demonstrating the performance of our system in a shopping arcade.