Authors:
Muhammad Habib Mahmood
1
and
Pere Ridao Rodriguez
2
Affiliations:
1
Air University and University of Girona, Pakistan
;
2
University of Girona, Spain
Keyword(s):
Localization, Mapping, Kalman Filter, Particle Filter, Occupancy Grid Mapping, Monte-Carlo Localization.
Related
Ontology
Subjects/Areas/Topics:
Applications
;
Computer Vision, Visualization and Computer Graphics
;
Human-Computer Interaction
;
Knowledge Acquisition and Representation
;
Methodologies and Methods
;
Motion and Tracking
;
Motion, Tracking and Stereo Vision
;
Pattern Recognition
;
Physiological Computing Systems
;
Robotics
;
Software Engineering
;
Theory and Methods
Abstract:
The advancements in robotics has given rise to the manufacturing of affordable educational mobile robots. Due to their size and cost, they possess limited global localization and mapping capability. The purpose of producing these robots is not fully materialized if advance algorithms cannot be demonstrated on them. In this paper, we address this limitation by just using dead-reckoning and low bandwidth noisy infrared sensors for localization in an unknown environment. We demonstrate Extended Kalman Filter implementation, produce a map of the unknown environment by Occupancy grid mapping and based on this map, perform particle filtering to do Monte-Carlo Localization. In our implementation, we use the low cost e-puck mobile robot, which performs these tasks. We also putforth an empirical evaluation of the results, which shows convergence. The presented results provide a base to further build on the navigation and path-planning problems.