Localisation and Mapping Using a Laser Range Finder: A Goal-Seeking Approach
- 1 January 2009
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- p. 270-276
- https://doi.org/10.1109/icas.2009.59
Abstract
In this paper we examine the problem of localisation and mapping of an unknown environment using data from a laser range finder. In order to support our method we detect landmarks in the environment using the same laser finder. For the localisation and mapping process to take place we assume that the mobile robot will follow a path until a landmark is observed by the laser scanner. Our approach alleviates the requirement to provide odometry or other information. In addition, an efficient path is sought to reach target location. An inherent property of this is obstacle avoidance. The simulated experiments presented in the paper validate the effectiveness of our approach.Keywords
This publication has 14 references indexed in Scilit:
- SLAM based on Kalman filter for multi-rate fusion of laser and encoder measurementsPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2005
- An efficient fastslam algorithm for generating maps of large-scale cyclic environments from raw laser range measurementsPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2004
- Mapping and localization with RFID technologyPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2004
- Map building without odometry informationPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2004
- Potential field methods and their inherent limitations for mobile robot navigationPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2002
- Object following and obstacle avoidance using a laser scanner in the outdoor mobile robot Auriga-αPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2002
- Real-time self-localization in unknown indoor environment using a panorama laser range finderPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2002
- Robust Monte Carlo localization for mobile robotsArtificial Intelligence, 2001
- Dynamic Map Building for an Autonomous Mobile RobotThe International Journal of Robotics Research, 1992
- On the Representation and Estimation of Spatial UncertaintyThe International Journal of Robotics Research, 1986