Markov Localization for Mobile Robots in Dynamic Environments
| Type of publication: | Article |
| Citation: | fox-etal-jair:99 |
| Journal: | Journal of Artificial Intelligence Research |
| Volume: | 11 |
| Year: | 1999 |
| Pages: | 391--427 |
| Abstract: | Localization, that is the estimation of a robot'slocation from sensor data, is a fundamental problem in mobilerobotics. This papers presents a version of Markov localization whichprovides accurate position estimates and which is tailored towardsdynamic environments. The key idea of Markov localization is tomaintain a probability density over the space of all locations of arobot in its environment. Our approach represents this spacemetrically, using a fine-grained grid to approximate densities. It isable to globally localize the robot from scratch and to recover fromlocalization failures. It is robust to approximate models of theenvironment (such as occupancy grid maps) and noisy sensors (such asultrasound sensors). Our approach also includes afiltering techniquewhich allows a mobile robot to reliably estimate its positioneven indensely populated environments in which crowds of people block therobot's sensors for extended periods of time. The method describedhere hasbeen implemented and tested in several real-worldapplications of mobile robots,including the deployments of two mobilerobots as interactive museum tour-guides. |
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