Robust Monte Carlo localization for mobile robots
Artificial Intelligence
Object Recognition from Local Scale-Invariant Features
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Multi-robot visual SLAM using a Rao-Blackwellized particle filter
Robotics and Autonomous Systems
LSH-RANSAC: an incremental scheme for scalable localization
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
| Hi-index | 0.00 |
The ability of finding its situation in a given environment is crucial for an autonomous agent. While navigating through a space, a mobile robot must be capable of finding its location in a map of the environment (i.e. its pose x, y, θ), otherwise, the robot will not be able to complete its task. This problem becomes specially challenging if the robot does not possess any external measure of its global position. Typically, dead-reckoning systems do fail in the estimation of robot's pose when working for long periods of time. In this paper we present a localization method based on the Monte Carlo algorithm. During the last decade this method has been extensively tested in the field of mobile Robotics, proving to be both robust and efficient. On the other hand, our approach takes advantage from the use of a vision sensor. In particular, we have chosen to use SIFT features as visual landmarks finding them suitable for the global localization of a mobile robot. We have succesfully tested our approach in a B21r mobile robot, achieving to globally localize the robot in few iterations. The technique is suitable for office-like environments and behaves correctly in the presence of people and moving objects.