A reliable scheme that deals with the map-building and the localization issues simultaneously is presented. The world-model estimate is fed to the localization algorithm, which in turn provides a corrected position and orientation estimate that is subsequently fed to the map-building algorithm to provide an updated world model. The perceived local world model along with dead-reckoning and ultrasonic sensor data are combined using an extended Kalman filter in a localization scheme to estimate the current position and orientation of the mobile robot. Implementation issues and experimental results from the experience with a Nomad 150 mobile robot in a real world indoor environment are presented.
ASJC Scopus subject areas
- Control and Systems Engineering
- Computer Science Applications
- Electrical and Electronic Engineering